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retoor af080fe68e Strict goal verification, approval hardening, retrieval eval, work limits, sandbox caps (1.38.0)

- Approval: re-prompt unknown answers (max 3), shell-only affirmative
  guidance recovery; destructive actions stay strict
- GoalVerifier subagent with strict JSON verdicts, bounded follow-up
  rounds, nonzero exit plus open turn on unverified one-shots
- Retrieval eval (Hit@K/MRR/nDCG/Context Precision), FTS cascade with
  title weighting, query/read event logging, --eval-retrieval CLI
- Supervisor caps (8 parallel, 64 records) and scheduler hardening
  (4 fires/tick, per-schedule lease, stated misfire policy)
- Sandbox box resource trio: 2g/2.0/256 at create, best-effort
  update for existing boxes, rm -f recovery pointer
- Research archive: 12 subjects, 667 sources, 27 conclusions;
  suites 979 + 16 green

2026-10-18 12:13:24 +02:00

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Effective context engineering for AI agents

Metadata

Field	Value
Institute	Molodetz Institute — Research & Retrieval Division
Subject	prompt-context-scaling
Document type	Source Dossier
Tier	1
Source title	Effective context engineering for AI agents
Source URL	https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
Source publisher / author	Anthropic
Source type	blog post
Source date (published)	2025-09-29
Date researched	2026-10-18
Researched by	tai (agent), model: Muse Spark, on behalf of retoor
Research method	direct page fetch
Confidence	High
Status	Active

Summary

Anthropic's context-engineering essay is the reference statement of the converged arsenal: context is finite with diminishing returns (context rot), so agents should compact near-full conversations into summaries, retrieve just-in-time via lightweight identifiers instead of stuffing, persist notes outside the window, and delegate deep work to subagents. Bigger windows do not remove the need for any of this.

Key findings

"As the number of tokens in the context window increases, the model's ability to accurately recall information from that context decreases" (context rot, observed across all models).
"Compaction is the practice of taking a conversation nearing the context window limit, summarizing its contents, and reinitiating a new context window with the summary", preserving decisions, bugs, and implementation details while discarding redundant tool outputs.
"Rather than pre-processing all relevant data up front, agents built with the 'just in time' approach maintain lightweight identifiers (file paths, stored queries, web links, etc.) and use these references to dynamically load data into context at runtime using tools."
Long-horizon techniques listed: "compaction, structured note-taking, and multi-agent architectures".
"Waiting for larger context windows might seem like an obvious tactic. But it's likely that for the foreseeable future, context windows of all sizes will be subject to context pollution and information relevance concerns."
Hybrid model endorsed: drop stable context up front, navigate the rest just-in-time with search primitives (their example: glob and grep over files).

Pros / strengths

Names every technique tai already implements (compaction, spill identifiers plus record_read, notes as records, fork subagents) as the recommended set, from the vendor operating the largest coding agent.
The just-in-time plus lightweight-identifier framing is exactly tai's mem: spill design, independently derived.

Cons / limitations / caveats

Vendor engineering essay, no controlled numbers; the compaction-recall tradeoff is stated qualitatively ("distills... in a high-fidelity manner") without a loss metric.
Assumes frontier-model summarization quality; tai compacts with whatever backend is active, including weak free tiers, where summary fidelity is lower.

Relevance to tai

The master checklist against which tai's context handling was audited: compaction (have it), just-in-time retrieval via identifiers (have it: spill ids plus search), notes outside the window (have it: records), subagent delegation (have it: fork/poll). The gap it exposes is oversized single inputs: Anthropic's techniques all assume the unit of work fits or can be fetched piecemeal, and tai has no path for one giant user prompt except holding it whole.

Changelog

Date	Change
2026-10-18	Initial dossier created.

3.5 KiB Raw Blame History