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Turing Test","\u002Fnews\u002Finsights\u002Fturing-tests","news\u002Finsights\u002Fturing-tests",{"title":250,"path":251,"stem":252},"Privacy Policy","\u002Fprivacy","privacy",{"title":254,"path":255,"stem":256},"Research","\u002Fresearch","research",{"title":258,"path":259,"stem":260},"Terms of Service","\u002Fterms","terms",{"id":262,"title":234,"body":263,"date":647,"description":648,"extension":649,"image":650,"meta":652,"navigation":663,"path":235,"seo":664,"stem":236,"__hash__":665},"insights\u002Fnews\u002Finsights\u002Fstructured-iteration-quality.md",{"type":264,"value":265,"toc":636},"minimark",[266,285,289,306,309,313,330,334,344,361,369,384,388,394,397,411,419,440,444,447,462,477,481,484,487,495],[267,268,271,272,271,278],"div",{"className":269},[270],"page-title","\n  ",[273,274,234],"h1",{"className":275,"id":277},[276],"page-title__main","the-turn-as-the-unit-of-quality",[279,280,284],"h2",{"className":281,"id":283},[282],"page-title__sub","what-makes-iterative-refinement-productive-and-when-it-starts-to-hurt","What makes iterative refinement productive, and when it starts to hurt",[286,287,288],"p",{},"Iterative refinement is one of the defining features of how language models are used in practice. Rather than producing a final result in a single pass, users and autonomous agents refine outputs across multiple turns of interaction. Early work on self-feedback and verbal reflection established that this approach reliably outperforms single-pass generation. But how reliably, and for how long?",[286,290,291,292,300,301,305],{},"A controlled study that ran 12-turn refinement conversations across ideation, code generation, and mathematical reasoning found that the answer depends almost entirely on what kind of feedback each turn provides ",[293,294,295],"sup",{},[296,297,299],"a",{"href":298},"#source-1","[1]",". In code and ideation tasks, gains arrived early and then plateaued. In math, late turns could still help, but only when the feedback was specific. Across all domains, vague instructions like \"improve it\" or \"make it better\" produced rapid saturation and output bloat. Targeted feedback addressing explicit quality dimensions, things like \"check whether the function handles the empty-list case\" or \"verify that the conclusion follows from the data in section two,\" sustained productive iteration further ",[293,302,303],{},[296,304,299],{"href":298},". The difference between useful iteration and destructive iteration had little to do with the number of turns taken. It had everything to do with what happened inside each one.",[286,307,308],{},"This finding connects three ideas that keep appearing across recent AI systems research. Structured checklists decompose quality into individually verifiable criteria, formalizing what \"targeted feedback\" actually means. Selective memory architectures decide what to retain and what to forget between turns, preventing the context window from becoming a graveyard of stale instructions. Deterministic validation layers enforce constraints that probabilistic models cannot guarantee on their own. Each imposes structure on what would otherwise be an open-ended, drift-prone process.",[279,310,312],{"id":311},"why-turns-go-wrong","Why Turns Go Wrong",[286,314,315,316,322,323,329],{},"Understanding why unstructured iteration degrades output requires looking at what happens inside a model's context window (the maximum amount of text a model can consider at once) as turns accumulate. Research on the \"lost in the middle\" phenomenon showed that language model performance is highest when relevant information appears at the beginning or end of the input, and drops significantly when the model must access information positioned in the middle of long contexts ",[293,317,318],{},[296,319,321],{"href":320},"#source-2","[2]",". As conversations grow longer, earlier instructions are not just diluted by newer content. The model's attention mechanism actively deprioritizes them. A survey covering over 1,400 research papers formalized this challenge by decomposing context engineering into three stages, retrieval, processing, and management, each introducing its own failure modes ",[293,324,325],{},[296,326,328],{"href":327},"#source-3","[3]",". The default mode of iterative interaction, appending each turn's output to a growing window without structured curation, is working against sustained quality from the start.",[279,331,333],{"id":332},"checklists-that-steer","Checklists That Steer",[267,335,337,338],{"style":336},"width: 100%; margin: 20px 0;","\n    ",[339,340],"img",{"src":341,"alt":342,"style":343},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1500627321089-19f8ec7b3038?w=800&auto=format&fit=crop","Close-up of an audio mixing console with rows of individual channel faders, analogous to how structured checklists give AI systems separate controls for each quality dimension rather than a single dial for overall improvement","width: 100%; height: auto;",[286,345,346,347,353,354,360],{},"A sound engineer at a mixing console adjusts each channel independently, setting levels for bass, treble, reverb, and compression on separate faders rather than turning a single \"make it sound better\" knob. Structured quality evaluation works the same way. The TICK framework demonstrated that decomposing quality into checklist-based yes\u002Fno questions is more reliable for both humans and language models than holistic scoring ",[293,348,349],{},[296,350,352],{"href":351},"#source-4","[4]",". Answering \"Does the response address the user's budget constraint?\" is a simpler cognitive task than assigning an overall quality rating on a 10-point scale. The decomposition reduces the inconsistency that plagues open-ended judgments, and composable pipelines like AutoChecklist can now generate such criteria automatically from a task description ",[293,355,356],{},[296,357,359],{"href":358},"#source-5","[5]",".",[286,362,363,364,368],{},"This connects directly to the 12-turn study's central finding. When Javaji et al. compared vague \"improve it\" feedback against prompts targeting specific quality dimensions, the targeted version sustained improvement over more turns precisely because it functioned as a single-item checklist ",[293,365,366],{},[296,367,299],{"href":298},". A multi-item checklist extends this logic by ordering quality dimensions by importance. Each turn addresses the highest-priority unsatisfied criterion, and the checklist records what has already been verified so that subsequent turns do not undo earlier gains. The model is no longer guessing what \"better\" means. The checklist tells it.",[286,370,371,372,378,379,383],{},"This pattern appears in practitioner tools as well. The Codified Context framework, developed during construction of a 108,000-line C# distributed system, included a \"constitution\" file that functioned as a prioritized checklist ",[293,373,374],{},[296,375,377],{"href":376},"#source-6","[6]",". Naming conventions came first, build commands second, orchestration protocols third. The ordering was not arbitrary. It reflected which violations were most costly to fix if left uncaught. Across 283 development sessions, this structure prevented repeated failures by ensuring each session validated high-priority constraints before moving to less critical ones ",[293,380,381],{},[296,382,377],{"href":376},". The criteria themselves can be generated by a model, but the prioritization, the decision about which quality dimension matters most, still required human judgment about costs and consequences.",[279,385,387],{"id":386},"remembering-what-matters","Remembering What Matters",[267,389,337,390],{"style":336},[339,391],{"src":392,"alt":393,"style":343},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1474932430478-367dbb6832c1?w=800&auto=format&fit=crop","Old books densely packed on a library shelf, analogous to how AI memory systems must decide which accumulated knowledge stays accessible and which can be safely let go to prevent valuable information from being buried",[286,395,396],{},"A library that never removes a book eventually buries its most valuable references under sheer accumulation. AI memory faces a similar problem. A checklist that structures each turn is only useful if the system remembers what was checked and what was found, but retaining everything introduces its own degradation.",[286,398,399,400,406,407,360],{},"The Agentic Context Engineering (ACE) framework named two failure modes that make this concrete ",[293,401,402],{},[296,403,405],{"href":404},"#source-7","[7]",". Brevity bias is the tendency for iterative optimization to compress rich context into short, generic summaries that strip away the domain-specific knowledge that actually made previous turns successful. A detailed playbook that says \"when the build fails on the orchestration layer, check the gRPC timeout before restarting the container\" gets summarized into \"handle build failures appropriately,\" and the specific knowledge that prevented a two-hour debugging session disappears. Context collapse is the complementary failure. Successive rewrites gradually erode important details, each individual edit seeming reasonable in isolation but the cumulative effect hollowing out the context's value ",[293,408,409],{},[296,410,405],{"href":404},[286,412,413,414,418],{},"ACE addressed both by treating context as an evolving playbook updated through structured, incremental additions rather than wholesale rewrites, achieving a 10.6% improvement over strong baselines ",[293,415,416],{},[296,417,405],{"href":404},". One counterintuitive finding from this work is that language models appear to perform better with long, detailed contexts than with tight summaries. Unlike humans, who benefit from concise briefings, LLMs can extract relevance from comprehensive inputs autonomously. Stripping context down for brevity's sake may sacrifice exactly the edge-case knowledge that separates correct output from output that merely compiles.",[286,420,421,422,428,429,433,434,360],{},"The Dynamic Cheatsheet (DC) framework demonstrates what effective curation looks like in practice ",[293,423,424],{},[296,425,427],{"href":426},"#source-8","[8]",". DC equips a language model with a persistent, self-curating external memory. After each query, the system explicitly decides which problem-solving strategies deserve to be kept, which should be discarded, and which existing entries should be updated. The results are impressive. On math competition problems, one model's accuracy more than doubled (from 23% to 50%) by retaining algebraic insights across problems. On the Game of 24 puzzle, another model went from 10% to 99% by accumulating and reusing solution templates ",[293,430,431],{},[296,432,427],{"href":426},". The gains did not come from better prompting or a larger model. They came from the system learning what was worth remembering, and what was not, across successive encounters with similar problems. Meta Context Engineering takes this one step further by having a separate agent optimize the curation procedures themselves, meaning even the format and structure of what gets remembered becomes subject to improvement ",[293,435,436],{},[296,437,439],{"href":438},"#source-9","[9]",[279,441,443],{"id":442},"hard-constraints-for-soft-outputs","Hard Constraints for Soft Outputs",[286,445,446],{},"Checklists and selective memory both improve iteration quality, but they share a limitation. Both rely on the language model itself, or a similar model, to make evaluative judgments. A model asked to evaluate its own output against a checklist can exhibit the same biases and inconsistencies that it exhibits in generation. For constraints that must hold without exception, a different mechanism is needed, one that removes the model from the decision entirely.",[286,448,449,450,456,457,461],{},"The general principle is to separate what the model does well (natural language understanding, flexible reasoning, tolerant interpretation of ambiguous input) from what it does poorly (logical guarantees, strict constraint enforcement). VERUS-LM demonstrates this by splitting reasoning into two responsibilities ",[293,451,452],{},[296,453,455],{"href":454},"#source-10","[10]",". The language model translates a task description into a formal representation. A symbolic reasoning engine then performs logically sound inference over that representation. On logical reasoning benchmarks, the advantage of this hybrid approach grew as task complexity increased ",[293,458,459],{},[296,460,455],{"href":454},". The model is good at understanding what the problem is. The symbolic engine is good at solving it correctly. Neither works as well alone.",[286,463,464,465,471,472,476],{},"An application of this division of labor uses the Lean 4 theorem prover as a verification layer for financial compliance ",[293,466,467],{},[296,468,470],{"href":469},"#source-11","[11]",". Every proposed action by the agent is translated into a formal logical proposition and verified by the Lean 4 proof kernel before execution. If the proof does not check, the action does not execute. There is no probability threshold, no confidence score, no \"this looks right.\" A compliance rule under this architecture becomes a constraint enforced with mathematical certainty, independent of whatever the model's next-token distribution might prefer ",[293,473,474],{},[296,475,470],{"href":469},". From a systems perspective, this is the kind of guarantee that makes the difference between a prototype and a production deployment in regulated industries.",[279,478,480],{"id":479},"what-this-suggests","What This Suggests",[286,482,483],{},"The three mechanisms operate at different stages of the refinement cycle and address distinct failure modes. A checklist defines what \"better\" means for the current turn. Selective memory decides what to carry forward. Deterministic validation enforces constraints that must hold regardless of the model's probabilistic output.",[286,485,486],{},"Any one of these in isolation appears to be insufficient. A checklist without selective memory will eventually be overwhelmed by accumulated context. Selective memory without structured criteria risks curating toward the wrong quality dimensions. Deterministic validation without good memory and good criteria will enforce hard constraints on output that is otherwise drifting.",[286,488,489,490,494],{},"For teams building iterative workflows, whether for code generation, research, writing, or any domain where quality develops through successive passes, the practical takeaway is that the turn is the unit of design. The effort spent deciding what each turn evaluates, remembers, and enforces may matter at least as much as the effort spent on the initial prompt. Whether the structuring of turns will itself be automated, as early work on meta-level skill evolution tentatively suggests ",[293,491,492],{},[296,493,439],{"href":438},", or whether it will remain a domain where human judgment about priorities and consequences provides durable value, is a question the field has not yet answered.",[267,496,271,500,271,503],{"className":497},[498,499],"references","mt-8",[279,501,502],{"id":498},"References",[504,505,337,511,337,529,337,541,337,552,337,563,337,574,337,584,337,595,337,606,337,616,337,626,271],"ol",{"className":506},[507,508,509,510],"list-decimal","list-inside","space-y-2","mt-4",[512,513,515,516,520,521],"li",{"id":514},"source-1","S. R. Javaji et al., \"Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting,\" in ",[517,518,519],"em",{},"Proc. NeurIPS 2025 Workshop on Multi-Turn Interactions with LLMs",", 2025, ",[296,522,528],{"href":523,"target":524,"className":525},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2509.06770","_blank",[526,527],"text-blue-600","underline","[Online]",[512,530,532,533,536,537],{"id":531},"source-2","N. F. Liu et al., \"Lost in the Middle: How Language Models Use Long Contexts,\" ",[517,534,535],{},"Transactions of the Association for Computational Linguistics",", vol. 12, pp. 157–173, 2024, ",[296,538,528],{"href":539,"target":524,"className":540},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172",[526,527],[512,542,544,545,520,548],{"id":543},"source-3","L. Mei et al., \"A Survey of Context Engineering for Large Language Models,\" ",[517,546,547],{},"arXiv",[296,549,528],{"href":550,"target":524,"className":551},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2507.13334",[526,527],[512,553,555,556,558,559],{"id":554},"source-4","J. Cook et al., \"TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation,\" ",[517,557,547],{},", 2024, ",[296,560,528],{"href":561,"target":524,"className":562},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.03608",[526,527],[512,564,566,567,569,570],{"id":565},"source-5","K. Zhou and C. Tan, \"AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge,\" ",[517,568,547],{},", 2026, ",[296,571,528],{"href":572,"target":524,"className":573},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.07019",[526,527],[512,575,577,578,569,580],{"id":576},"source-6","A. Vasilopoulos, \"Codified Context: Infrastructure for AI Agents in a Complex Codebase,\" ",[517,579,547],{},[296,581,528],{"href":582,"target":524,"className":583},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.20478",[526,527],[512,585,587,588,569,591],{"id":586},"source-7","Q. Zhang et al., \"Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models,\" in ",[517,589,590],{},"Proc. International Conference on Learning Representations (ICLR)",[296,592,528],{"href":593,"target":524,"className":594},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.04618",[526,527],[512,596,598,599,569,602],{"id":597},"source-8","M. Suzgun et al., \"Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory,\" in ",[517,600,601],{},"Proc. European Chapter of the Association for Computational Linguistics (EACL)",[296,603,528],{"href":604,"target":524,"className":605},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.07952",[526,527],[512,607,609,610,569,612],{"id":608},"source-9","H. Ye et al., \"Meta Context Engineering via Agentic Skill Evolution,\" ",[517,611,547],{},[296,613,528],{"href":614,"target":524,"className":615},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2601.21557",[526,527],[512,617,619,620,520,622],{"id":618},"source-10","B. Callewaert, S. Vandevelde, and J. Vennekens, \"VERUS-LM: A Versatile Framework for Combining LLMs with Symbolic Reasoning,\" ",[517,621,547],{},[296,623,528],{"href":624,"target":524,"className":625},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.14540",[526,527],[512,627,629,630,569,632],{"id":628},"source-11","D. Rashie and V. Rashi, \"Type-Checked Compliance: Deterministic Guardrails for Agentic Financial Systems Using Lean 4 Theorem Proving,\" ",[517,631,547],{},[296,633,528],{"href":634,"target":524,"className":635},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2604.01483",[526,527],{"title":637,"searchDepth":638,"depth":638,"links":639},"",2,[640,641,642,643,644,645,646],{"id":283,"depth":638,"text":284},{"id":311,"depth":638,"text":312},{"id":332,"depth":638,"text":333},{"id":386,"depth":638,"text":387},{"id":442,"depth":638,"text":443},{"id":479,"depth":638,"text":480},{"id":498,"depth":638,"text":502},"2026-05-04","Iterative refinement with language models can improve or degrade output depending on what happens inside each turn. Structured checklists, selective memory, and deterministic validation are three mechanisms that determine whether successive passes build quality or erode it.","md",{"src":651},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1759148414274-c8df3fb77f8c?w=800&auto=format&fit=crop",{"authors":653,"badge":659,"source":661},[654],{"avatar":655,"name":657,"to":658},{"src":656},"\u002Fimg\u002Fmark_avatar.png","Mark Williams","https:\u002F\u002Fthinkata.com",{"label":660},"AI Engineering",{"name":662,"url":658},"Thinkata Research",true,{"title":234,"description":648},"4ivaNfcPk1nk_mVk9hZ7-G2llRUC36tffLkhX5QUrpM",[667,968],{"id":668,"title":142,"body":669,"date":955,"description":956,"extension":649,"image":957,"meta":959,"navigation":663,"path":143,"seo":966,"stem":144,"__hash__":967,"_path":143},"insights\u002Fnews\u002Finsights\u002Fcontext-is-infrastructure.md",{"type":264,"value":670,"toc":947},[671,683,691,694,704,708,716,724,731,744,748,761,782,795,808,812,819,827,853,856,860,863,869,872,875],[267,672,271,674,271,678],{"className":673},[270],[273,675,142],{"className":676,"id":677},[276],"context-is-infrastructure-not-instructions",[279,679,682],{"className":680,"id":681},[282],"what-teams-gain-when-they-govern-ai-context-like-a-software-dependency","What teams gain when they govern AI context like a software dependency",[286,684,685,686,690],{},"A team replaces task-specific prompts with a generic \"improved\" template. Extraction accuracy drops from 100% to 90%. RAG compliance (the degree to which a model's answers stay grounded in retrieved documents rather than generating from its own training data) falls from 93.3% to 80% ",[293,687,688],{},[296,689,299],{"href":298},". The model is the same. The new instructions look better on paper. What changed was the context, and nobody tested whether the change was safe before deploying it.",[286,692,693],{},"This is context regression, a term borrowed from software engineering where \"regression\" means a change that was supposed to improve something but degraded existing behavior instead. It behaves like any other dependency compatibility problem in a software supply chain, and the governance response, production contracts, risk-based test suites, compatibility gates, is the same one software teams already use for their other dependencies.",[286,695,696,699,700,703],{},[296,697,698],{"href":147},"\"Context is the New Code\""," established context engineering as a formal discipline with its own taxonomy, maturity levels, and practitioner artifacts, and ",[296,701,702],{"href":235},"\"The Turn as the Unit of Quality\""," explored how structured iteration with checklists and selective memory improves turn-level quality. This article picks up a different thread. What happens when context moves from a single team's configuration file to an organizational dependency serving dozens of agents across thousands of daily interactions? Recent research suggests that the teams making the fastest progress are the ones applying familiar software supply chain governance to their context, and the returns are measurable.",[279,705,707],{"id":706},"what-structured-context-unlocks","What Structured Context Unlocks",[286,709,710,711,715],{},"A study of 200 documented interactions across four AI tools found that incomplete context was associated with 72% of iteration cycles ",[293,712,713],{},[296,714,321],{"href":320},". That number is worth sitting with. Nearly three-quarters of the rework, the back-and-forth where a human corrects, clarifies, and re-prompts, traced not to a bad model or a poorly worded instruction but to missing information that should have been available from the start.",[286,717,718,719,723],{},"When the same study introduced structured context assembly, a methodology that organizes context into five roles (Authority, Exemplar, Constraint, Rubric, and Metadata), iteration cycles dropped from an average of 3.8 to 2.0 per task, and first-pass acceptance rose from 32% to 55% ",[293,720,721],{},[296,722,321],{"href":320},". Authority context establishes what standards govern the task. Exemplar context provides reference outputs that demonstrate the expected quality. Constraint context defines boundaries the output must respect. Rubric context specifies how the output will be evaluated. Metadata context supplies facts, dates, names, and domain-specific details. Having names for these roles is not a minor convenience, it is what makes the difference between ad hoc tuning and repeatable engineering, because a team that cannot describe what is missing from its context cannot systematically fix it.",[267,725,337,726],{"style":336},[339,727],{"src":728,"alt":729,"style":730},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1639066648921-82d4500abf1a?w=800&auto=format&fit=crop","Rows of server equipment in a data center, analogous to how structured context engineering creates organized, reliable infrastructure rather than ad hoc configurations","width: 100%; height: 320px; object-fit: cover; object-position: center;",[286,732,733,734,738,739,743],{},"Like a well-organized server room where every cable run is labeled and every rack follows a standard layout, structured context gives a team the ability to reason about what the AI is actually working with. The evaluation-driven iteration research reinforces this by showing that context quality is not one-dimensional ",[293,735,736],{},[296,737,299],{"href":298},". A change that improves instruction-following can simultaneously degrade extraction accuracy. A prompt that scores better on helpfulness can score worse on format compliance. The minimum viable evaluation suite (MVES) framework proposes tiered evaluation requirements, one set for general applications, another for retrieval-augmented generation systems, and a third for agentic workflows, precisely because quality along one dimension does not guarantee quality along others ",[293,740,741],{},[296,742,299],{"href":298},". The practical implication is that quality has multiple dimensions that can trade against each other, and navigating those trade-offs requires measurement infrastructure, not intuition.",[279,745,747],{"id":746},"governing-context-as-a-dependency","Governing Context as a Dependency",[286,749,750,751,755,756,760],{},"The clearest articulation of this shift comes from research that frames LLM update management as a software supply chain governance problem ",[293,752,753],{},[296,754,328],{"href":327},". Hosted language model services evolve through provider-side updates without explicit version changes, so the API endpoint stays the same while the behavior underneath shifts. Empirical work cited within that framework documents cases where code execution accuracy dropped from 52% to 10% within three months with no version change on the consumer side ",[293,757,758],{},[296,759,405],{"href":404},". This is behavioral drift (a gradual, unannounced change in how a model responds to the same inputs), and it affects every piece of context that was tuned against the previous behavior.",[286,762,763,764,768,769,773,774,777,778,781],{},"The proposed governance framework has three components that map directly to established software engineering practice ",[293,765,766],{},[296,767,328],{"href":327},". ",[770,771,772],"strong",{},"Production contracts"," define explicit behavioral rules with measurable thresholds, things like \"authentication code must pass security tests\" or \"JSON outputs must be valid.\" ",[770,775,776],{},"Risk-category-based testing"," organizes evaluation around deployment risk areas rather than relying on a single aggregate score, preventing critical regressions in formatting or safety from being masked by overall performance improvements. ",[770,779,780],{},"Compatibility gates"," block updates that fail defined thresholds, requiring review before a model update is adopted into production. None of these ideas are new to software engineering. What is new is recognizing that context, the system prompts, retrieved documents, and configuration files that shape AI behavior, is a dependency that deserves the same governance.",[286,783,784,785,789,790,794],{},"A readiness harness for LLM and RAG applications demonstrates what this looks like in practice ",[293,786,787],{},[296,788,352],{"href":351},". The system combines automated benchmarks, OpenTelemetry observability (a standardized way to collect and export telemetry data like traces, metrics, and logs), and CI quality gates (automated checkpoints in the deployment pipeline that block releases if quality checks fail) under a minimal API contract. Rather than reducing readiness to a single metric, it aggregates workflow success, policy compliance, groundedness, retrieval hit rate, cost, and latency into scenario-weighted readiness scores. In ticket-routing experiments, the regression gates consistently rejected unsafe prompt variants before deployment ",[293,791,792],{},[296,793,352],{"href":351},". This is a concrete example of the shift from \"the model was tested\" to \"the deployment pipeline tested every context change before it reached production.\"",[286,796,797,798,802,803,807],{},"One challenge specific to AI systems is that the same configuration can produce different outputs across runs. Traditional binary pass\u002Ffail testing struggles with this fundamental non-determinism. A regression testing framework designed for this problem replaces binary verdicts with three-valued probabilistic outcomes (Pass, Fail, Inconclusive) backed by confidence intervals and sequential analysis ",[293,799,800],{},[296,801,359],{"href":358},". The framework achieves 78 to 100% cost reduction compared to naive repeated testing while maintaining statistical guarantees, and its behavioral fingerprinting approach achieves 86% detection power on regressions where binary pass\u002Ffail testing has 0% ",[293,804,805],{},[296,806,359],{"href":358},". The cost reduction matters as much as the accuracy. Testing that is too expensive to run routinely is testing that does not get run, and context changes that do not get tested are the ones that cause production surprises.",[279,809,811],{"id":810},"from-files-to-living-systems","From Files to Living Systems",[286,813,814,815,360],{},"The governance patterns above treat context as a versioned artifact, something written, tested, and deployed. But a growing body of work suggests that this framing, while useful, captures only part of the picture. In production multi-agent systems, context is not a file. It is a runtime-constructed \"View\" projected into an agent's context window (the maximum amount of text a model can consider at once) from a pool of global artifacts, and that View changes dynamically based on the task, the step, and the state of the system ",[293,816,817],{},[296,818,377],{"href":376},[286,820,821,822,826],{},"Research on what the authors call \"Loosely-Structured Software\" characterizes this as a class of system whose defining property is runtime generation and evolution under uncertainty ",[293,823,824],{},[296,825,377],{"href":376},". Classic software architecture assumes build-time decomposition and slow-changing boundaries. Multi-agent AI systems violate those assumptions in three ways. First, an agent's effective program is determined not by compiled code but by a View assembled at runtime from system prompts, skills, plans, tools, and memories. Second, the connections between components form dynamically through semantic understanding rather than fixed function signatures. Third, the system's own executable substrate, the artifacts that mediate its behavior, can be rewritten by the system itself.",[286,828,829,830,768,834,837,838,840,841,844,845,848,849,852],{},"To make this governable, the research proposes a three-layer engineering framework ",[293,831,832],{},[296,833,377],{"href":376},[770,835,836],{},"View\u002FContext Engineering"," manages the execution environment and maintains task-relevant Views. This is the layer where the static context files that teams already write (the CLAUDE.md and AGENTS.md files examined in ",[296,839,698],{"href":147},") get assembled, filtered, and delivered at runtime. ",[770,842,843],{},"Structure Engineering"," organizes the dynamic bindings between agents and artifacts, governing how components find and connect to each other. ",[770,846,847],{},"Evolution Engineering"," manages the lifecycle of self-rewriting artifacts, ensuring that when the system modifies its own context (a capability that ",[296,850,851],{"href":159},"\"The Edge of the Underdefined\""," documents self-improving agents already demonstrating), those modifications remain within governed bounds.",[286,854,855],{},"This is where context infrastructure becomes genuinely adaptive. Instead of choosing between static configuration files (reliable but rigid) and autonomous self-modification (flexible but ungoverned), the three-layer framework offers a middle path. Context can evolve in response to operational feedback, while infrastructure constraints prevent that evolution from drifting outside acceptable bounds. The combination of governance patterns from the supply chain framing with the runtime adaptivity from the loosely-structured software framing points toward a more complete picture of what production context infrastructure might look like.",[279,857,859],{"id":858},"the-maturity-opportunity","The Maturity Opportunity",[286,861,862],{},"The infrastructure patterns described here, production contracts, multi-dimensional evaluation, CI gates, statistical regression testing, runtime View management, each have working implementations backed by empirical evidence. The gap between what the research demonstrates and what most teams have actually built is mostly one of adoption, not of available tools.",[286,864,865,866,868],{},"Survey data suggests that prompt usage in software engineering remains largely ad hoc, with prompts refined through trial-and-error and rarely reused. As ",[296,867,698],{"href":147}," noted, only about 5% of surveyed open-source repositories have adopted any context file format at all. The parallel to early unit testing adoption or early version control adoption is hard to miss. A practice that starts as optional among a skilled minority tends to become standard once enough teams experience the cost of not doing it.",[286,870,871],{},"What distinguishes this moment is that the infrastructure does not need to be invented from scratch. Supply chain governance, production testing methodology, continuous deployment practice, and statistical experiment design all have established patterns that transfer directly to context management. Treating context as infrastructure is largely a matter of applying existing engineering discipline to a new class of artifact, one that happens to shape every decision an AI system makes.",[286,873,874],{},"The teams moving fastest appear to be the ones that recognized this early. They built the infrastructure to measure, test, and govern the context their models consume, and that investment compounded over time. For teams still tuning prompts by hand and evaluating by feel, the patterns are available to adopt directly, without rediscovering the hard lessons from scratch.",[267,876,271,878,271,880],{"className":877},[498,499],[279,879,502],{"id":498},[504,881,337,883,337,892,337,901,337,910,337,919,337,928,337,937,271],{"className":882},[507,508,509,510],[512,884,885,886,569,888],{"id":514},"D. Commey, \"When 'Better' Prompts Hurt: Evaluation-Driven Iteration for LLM Applications,\" ",[517,887,547],{},[296,889,528],{"href":890,"target":524,"className":891},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2601.22025",[526,527],[512,893,894,895,569,897],{"id":531},"E. Calboreanu, \"Context Engineering: A Practitioner Methodology for Structured Human-AI Collaboration,\" ",[517,896,547],{},[296,898,528],{"href":899,"target":524,"className":900},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2604.04258",[526,527],[512,902,903,904,569,906],{"id":543},"M. S. Chishti et al., \"Test Before You Deploy: Governing Updates in the LLM Supply Chain,\" ",[517,905,547],{},[296,907,528],{"href":908,"target":524,"className":909},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2604.27789",[526,527],[512,911,912,913,569,915],{"id":554},"A. C. Maiorano, \"LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM\u002FRAG Applications,\" ",[517,914,547],{},[296,916,528],{"href":917,"target":524,"className":918},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.27355",[526,527],[512,920,921,922,569,924],{"id":565},"V. P. Bhardwaj, \"AgentAssay: Token-Efficient Regression Testing for Non-Deterministic AI Agent Workflows,\" ",[517,923,547],{},[296,925,528],{"href":926,"target":524,"className":927},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.02601",[526,527],[512,929,930,931,569,933],{"id":576},"W. Zhang et al., \"Loosely-Structured Software: Engineering Context, Structure, and Evolution Entropy in Runtime-Rewired Multi-Agent Systems,\" ",[517,932,547],{},[296,934,528],{"href":935,"target":524,"className":936},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.15690",[526,527],[512,938,939,940,942,943],{"id":586},"L. Chen et al., \"How Is ChatGPT's Behavior Changing over Time?,\" ",[517,941,547],{},", 2023, ",[296,944,528],{"href":945,"target":524,"className":946},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09009",[526,527],{"title":637,"searchDepth":638,"depth":638,"links":948},[949,950,951,952,953,954],{"id":681,"depth":638,"text":682},{"id":706,"depth":638,"text":707},{"id":746,"depth":638,"text":747},{"id":810,"depth":638,"text":811},{"id":858,"depth":638,"text":859},{"id":498,"depth":638,"text":502},"2026-05-09","Most teams treat AI context as a runtime concern, something to tune session by session. The teams making the fastest progress treat it as a software dependency, versioned, tested, and governed. The infrastructure patterns for doing this already exist.",{"src":958},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1558494949-ef010cbdcc31?w=800&auto=format&fit=crop",{"authors":960,"badge":964,"source":965},[961],{"avatar":962,"name":657,"to":963},{"src":656},"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fmarkwilliamsthinkata\u002F",{"label":660},{"name":662,"url":658},{"title":142,"description":956},"ytvzj-4FpQSyhlfi_1zbQOsnMlIjEix-h83JPRQFEN8",{"id":969,"title":74,"body":970,"date":1255,"description":1256,"extension":649,"image":1257,"meta":1259,"navigation":663,"path":75,"seo":1265,"stem":76,"__hash__":1266,"_path":75},"insights\u002Fnews\u002Finsights\u002Fagent-capability-reliability-split.md",{"type":264,"value":971,"toc":1237},[972,984,987,990,994,1002,1007,1014,1018,1026,1029,1033,1041,1061,1068,1072,1080,1088,1092,1105,1109,1117,1121,1129,1133,1136,1139],[267,973,271,975,271,979],{"className":974},[270],[273,976,74],{"className":977,"id":978},[276],"the-capability-reliability-split-in-agent-systems",[279,980,983],{"className":981,"id":982},[282],"why-frontier-agents-reach-state-of-the-art-on-one-run-and-fail-at-the-same-task-on-the-next","Why frontier agents reach state-of-the-art on one run, and fail at the same task on the next",[286,985,986],{},"A frontier agent can occasionally surpass a published research baseline and, in another run on the same task, fail to make any meaningful progress. The pattern recurs often enough across recent evaluations that researchers have started to treat it as a structural feature of agent systems rather than a quirk of any single implementation. Capability asks whether a model can perform a task in principle. Reliability asks whether it does so consistently, across repeated attempts, across small perturbations, and across tasks that take dozens or hundreds of steps to complete. Recent evidence suggests these two properties drift apart faster than benchmark headlines make visible.",[286,988,989],{},"The split has practical stakes. An agent system, in this context, refers to a large language model (LLM, the underlying neural network that processes text) coupled with a scaffold (the surrounding software that decides when to call the model, what tools to invoke, and how to handle errors). When the same agent passes a benchmark on Monday and breaks on a near-identical task on Tuesday, the deployment question is no longer whether the technology can do the work. The question becomes how often it does.",[279,991,993],{"id":992},"when-the-same-agent-both-wins-and-fails","When the Same Agent Both Wins and Fails",[286,995,996,997,1001],{},"ResearchGym, a benchmark that places agents inside containerized research environments rebuilt from accepted papers at ICML, ICLR, and ACL, captures the split with unusual clarity. In a controlled evaluation of an agent powered by GPT-5, the system improved over the provided baselines in only 1 of 15 evaluations, an improvement rate of 6.7%, and completed only 26.5% of sub-tasks on average across 39 sub-tasks total ",[293,998,999],{},[296,1000,299],{"href":298},". In a single run, the same agent surpassed the solution from an ICML 2025 Spotlight paper, evidence that the underlying capability is real even when the reliability is not. Proprietary scaffolds built on Claude Code (Opus-4.5) and Codex (GPT-5.2) displayed a similar gap.",[1003,1004,1006],"h3",{"id":1005},"across-long-horizons","Across Long Horizons",[286,1008,1009,1010,360],{},"HORIZON, a cross-domain diagnostic benchmark released in April 2026, looked at the same problem from a different angle. Across more than 3,100 trajectories collected from frontier models in the GPT-5 and Claude families, the authors documented a horizon-dependent degradation pattern. Agents that performed strongly on short tasks broke down on long-horizon work that required extended, interdependent action sequences ",[293,1011,1012],{},[296,1013,321],{"href":320},[1003,1015,1017],{"id":1016},"across-many-models","Across Many Models",[286,1019,1020,1021,1025],{},"The Holistic Agent Leaderboard (HAL), introduced by a group at Princeton, ran 21,730 agent rollouts spanning 9 models, 9 benchmarks, and four domains, comparing models, scaffolds, and benchmarks side by side and bringing the cost of large-scale agent evaluation down by roughly an order of magnitude ",[293,1022,1023],{},[296,1024,328],{"href":327},". One counterintuitive finding from that data is worth pausing on. Higher reasoning effort, the practice of allocating more inference-time compute to deliberation, reduced accuracy in the majority of runs.",[286,1027,1028],{},"A move that should obviously help did not. Bigger headline numbers and steadier behavior are not the same thing, even when the same lever is being pulled.",[279,1030,1032],{"id":1031},"why-standard-benchmarks-miss-the-gap","Why Standard Benchmarks Miss the Gap",[286,1034,1035,1036,1040],{},"Part of the reliability story is methodological. Most agent evaluations report pass@1, the probability that an agent succeeds on a single attempt. A 2026 study collected 60,000 agentic trajectories on SWE-Bench-Verified, a software engineering benchmark, across three models and two scaffolds, and found that single-run pass@1 estimates vary by 2.2 to 6.0 percentage points depending on which run is selected, with standard deviations exceeding 1.5 percentage points even at temperature 0, the setting that should produce the most deterministic behavior ",[293,1037,1038],{},[296,1039,352],{"href":351},". Reported improvements of 2 to 3 percentage points, the kind that often headline a new release, may reflect evaluation noise rather than genuine progress. Trajectories diverged early, often within the first few percent of generated tokens (a token is the unit of text the model processes, roughly a word or word fragment), and these small differences cascaded into entirely different solution strategies.",[267,1042,337,1043,337,1047,337,1052],{"style":336},[339,1044],{"src":1045,"alt":1046,"style":343},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1540962351504-03099e0a754b?w=800&auto=format&fit=crop","Aircraft cockpit instrument panel with multiple separate gauges and dials, analogous to how a reliability science framework breaks agent performance into independent measurements rather than a single accuracy score",[1003,1048,1051],{"style":1049,"id":1050},"margin: 1rem 0 0.5rem 0;","a-reliability-science-for-agents","A Reliability Science for Agents",[286,1053,1055,1056,1060],{"style":1054},"margin: 0;","Just as a cockpit instrument panel separates altitude, airspeed, and fuel into independent gauges so a pilot can see when one is failing, a reliability science framework released in March 2026 splits agent performance into separate dimensions tracked over time. The authors evaluated 10 models across 23,392 episodes on a 396-task benchmark that varied task duration and domain, and proposed four metrics including a Reliability Decay Curve, which tracks how success rate falls as tasks lengthen, and a Variance Amplification Factor, which measures how variability in outcomes grows with horizon ",[293,1057,1058],{},[296,1059,359],{"href":358},". Capability and reliability rankings diverged substantially, with multi-rank inversions at long horizons. A model ranked first on short tasks could fall to fourth or fifth once tasks stretched out. Frontier models showed the highest meltdown rates, up to 19%, because they attempted ambitious multi-step strategies that sometimes spiraled into failure.",[286,1062,1063,1064,360],{},"A March 2025 survey of agent evaluation methods, updated through 2026, identified the same pattern at a higher level. Cost-efficiency, safety, and robustness remain underassessed in most agent benchmarks ",[293,1065,1066],{},[296,1067,377],{"href":376},[279,1069,1071],{"id":1070},"the-mechanics-of-long-horizon-failure","The Mechanics of Long-Horizon Failure",[286,1073,1074,1075,1079],{},"The next question is mechanical. What is actually breaking when an agent that performs well on short tasks falls apart on long ones? A January 2026 analysis frames the answer as a mismatch between reasoning and planning. Step-wise reasoning, the chain-of-thought pattern that has driven much of the recent progress in LLMs, induces what the authors call a step-wise greedy policy ",[293,1076,1077],{},[296,1078,405],{"href":404},". The agent picks the locally best next action without modeling delayed consequences. Over short horizons this often suffices. Over long horizons, early myopic commitments compound and become difficult to recover from. The proposed fix, FLARE (Future-aware Lookahead with Reward Estimation), pushes value propagation back through the trajectory so that downstream outcomes can shape early decisions. Across multiple benchmarks, FLARE often allowed a smaller open-source model to outperform a larger frontier model running standard step-by-step reasoning. The argument draws a clearer line between reasoning, the local manipulation of intermediate steps, and planning, the explicit consideration of how early choices constrain later ones.",[286,1081,1082,1083,1087],{},"ResearchGym catalogs the same phenomenon from the failure side. Across runs, the recurring problems were impatience, poor time and resource management, overconfidence in weak hypotheses, difficulty coordinating parallel experiments, and hard limits imposed by context length, the maximum number of tokens an LLM can consider at once ",[293,1084,1085],{},[296,1086,299],{"href":298},". None of these are pure capability failures. An agent that knows what a good experiment looks like can still abandon it too early, commit to the wrong hypothesis with too much confidence, or simply run out of working memory before the task ends. The capabilities the model has in isolation do not translate cleanly into behavior under sustained pressure.",[279,1089,1091],{"id":1090},"what-helps-and-what-surprisingly-does-not","What Helps, and What Surprisingly Does Not",[286,1093,1094,1095,1099,1100,1104],{},"Mitigation research has clustered around test-time scaling, the practice of allocating more compute at inference time to improve outcomes without retraining. The first systematic study of test-time scaling for language agents, published in mid-2025, found that scaling helps, that knowing when to reflect matters, that list-wise verification methods, which compare a list of candidates rather than ranking them pairwise, outperform alternatives, and that diversifying rollouts has a positive effect on task performance ",[293,1096,1097],{},[296,1098,427],{"href":426},". A 2026 framework called ARTIS extended these ideas to settings where actions touch external systems and cannot be undone, by decoupling exploration from commitment through simulated interactions before real-world execution ",[293,1101,1102],{},[296,1103,439],{"href":438},". The authors flag a less obvious finding. Naive LLM-based simulators struggle to capture rare but high-impact failure modes, which means simulators have to be deliberately trained to be honest about how things go wrong, not only how they go right.",[1003,1106,1108],{"id":1107},"what-helps","What Helps",[286,1110,1111,1112,1116],{},"For long-horizon coding agents specifically, a 2026 study argued that test-time scaling is fundamentally a problem of representation, selection, and reuse rather than generating more attempts ",[293,1113,1114],{},[296,1115,455],{"href":454},". By converting each rollout into a structured summary of hypotheses, progress, and failure modes, then using methods like Recursive Tournament Voting and Parallel-Distill-Refine to select among candidates, the authors moved Claude-4.5-Opus from 70.9% to 77.6% on SWE-Bench Verified and from 46.9% to 59.1% on Terminal-Bench v2.0.",[1003,1118,1120],{"id":1119},"what-hurts","What Hurts",[286,1122,1123,1124,1128],{},"The same reliability framework that documented divergence between capability and reliability also reported a counterintuitive negative result. Across all 10 models tested, memory scaffolds, the systems designed to give agents persistent context across turns, universally hurt long-horizon performance ",[293,1125,1126],{},[296,1127,359],{"href":358},". The default assumption that more memory is always better appears to be wrong in this regime, at least for the scaffolds and tasks studied. The HAL finding that higher reasoning effort can reduce accuracy points in a similar direction. More of a thing is not always more useful.",[279,1130,1132],{"id":1131},"what-this-might-mean","What This Might Mean",[286,1134,1135],{},"The picture that emerges, while still incomplete, points toward a few useful adjustments rather than a single fix. The field appears to be moving toward treating reliability as a first-class evaluation dimension rather than a footnote to capability. Multi-run pass@1, statistical power analysis, and pessimistic bounds like pass^k are entering the conversation precisely because the cost of mistaking noise for progress is now visible. The design assumption that more compute, more memory, or more reasoning effort always helps is being tested empirically and sometimes failing. The gap between \"the agent did this once\" and \"the agent does this when it matters\" remains the gap that separates impressive demos from production deployments.",[286,1137,1138],{},"For organizations evaluating agent systems, the implication is straightforward enough to state without overstatement. A single high score on a benchmark suggests what the system can sometimes do. It does not, on its own, describe what the system will do under repetition, perturbation, or duration. The evidence from late 2025 and early 2026 suggests treating these as different questions, and budgeting evaluation accordingly. One open question is whether the next generation of agent improvements will close the split or widen it.",[267,1140,271,1142,271,1144],{"className":1141},[498,499],[279,1143,502],{"id":498},[504,1145,337,1147,337,1156,337,1165,337,1174,337,1183,337,1192,337,1201,337,1210,337,1219,337,1228,271],{"className":1146},[507,508,509,510],[512,1148,1149,1150,569,1152],{"id":514},"A. Garikaparthi et al., \"ResearchGym: Evaluating Language Model Agents on Real-World AI Research,\" ",[517,1151,547],{},[296,1153,528],{"href":1154,"target":524,"className":1155},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.15112",[526,527],[512,1157,1158,1159,569,1161],{"id":531},"X. J. Wang et al., \"The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break,\" ",[517,1160,547],{},[296,1162,528],{"href":1163,"target":524,"className":1164},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2604.11978",[526,527],[512,1166,1167,1168,520,1170],{"id":543},"S. Kapoor et al., \"Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation,\" ",[517,1169,547],{},[296,1171,528],{"href":1172,"target":524,"className":1173},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.11977",[526,527],[512,1175,1176,1177,569,1179],{"id":554},"B. Bjarnason et al., \"On Randomness in Agentic Evals,\" ",[517,1178,547],{},[296,1180,528],{"href":1181,"target":524,"className":1182},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.07150",[526,527],[512,1184,1185,1186,569,1188],{"id":565},"A. Khanal et al., \"Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents,\" ",[517,1187,547],{},[296,1189,528],{"href":1190,"target":524,"className":1191},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.29231",[526,527],[512,1193,1194,1195,520,1197],{"id":576},"A. Yehudai et al., \"Survey on Evaluation of LLM-based Agents,\" ",[517,1196,547],{},[296,1198,528],{"href":1199,"target":524,"className":1200},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2503.16416",[526,527],[512,1202,1203,1204,569,1206],{"id":586},"Z. Wang et al., \"Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents,\" ",[517,1205,547],{},[296,1207,528],{"href":1208,"target":524,"className":1209},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2601.22311",[526,527],[512,1211,1212,1213,520,1215],{"id":597},"K. Zhu et al., \"Scaling Test-time Compute for LLM Agents,\" ",[517,1214,547],{},[296,1216,528],{"href":1217,"target":524,"className":1218},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.12928",[526,527],[512,1220,1221,1222,569,1224],{"id":608},"X. Zeng et al., \"ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation,\" ",[517,1223,547],{},[296,1225,528],{"href":1226,"target":524,"className":1227},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.01709",[526,527],[512,1229,1230,1231,569,1233],{"id":618},"J. Kim et al., \"Scaling Test-Time Compute for Agentic Coding,\" ",[517,1232,547],{},[296,1234,528],{"href":1235,"target":524,"className":1236},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2604.16529",[526,527],{"title":637,"searchDepth":638,"depth":638,"links":1238},[1239,1240,1245,1248,1249,1253,1254],{"id":982,"depth":638,"text":983},{"id":992,"depth":638,"text":993,"children":1241},[1242,1244],{"id":1005,"depth":1243,"text":1006},3,{"id":1016,"depth":1243,"text":1017},{"id":1031,"depth":638,"text":1032,"children":1246},[1247],{"id":1050,"depth":1243,"text":1051},{"id":1070,"depth":638,"text":1071},{"id":1090,"depth":638,"text":1091,"children":1250},[1251,1252],{"id":1107,"depth":1243,"text":1108},{"id":1119,"depth":1243,"text":1120},{"id":1131,"depth":638,"text":1132},{"id":498,"depth":638,"text":502},"2026-04-25","Why frontier agents reach state-of-the-art on one run and fail at the same task on the next, and what evaluation needs to change.",{"src":1258},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1518770660439-4636190af475?w=800&auto=format&fit=crop",{"authors":1260,"badge":1263,"source":1264},[1261],{"avatar":1262,"name":657,"to":658},{"src":656},{"label":660},{"name":662,"url":658},{"title":74,"description":1256},"0a5e4zWiRLVunzuNxOgmhm3xM6u7S8XiD0kTkSLs9Go",1778947327125]