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AI Agent engineering reading list, Sep 30–Oct 1, 2026 Five updates on model routing, sandbox snapshots, Cloud CLI MCP, Data Agent Kit, and sending production failures directly to coding agents.
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AI Agent engineering reading list, Sep 29–30, 2026 Seven readings on browser authorization, task cost, isolated workspaces, MCP events, runtime safety, and multi-agent routing, with source limits intact.
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Keep review evidence separate from compacted agent context Independent Codex Guardian history shows why generation context and review evidence need separate records, with provenance through resume and rollback.
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AI Agent engineering reading list, Sep 26–27, 2026 Five updates on tool and skill context budgets, context doctor, persistent scheduling, tool-loop limits, and fail-closed approvals.
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Why old CLAUDE.md rules can hinder a model upgrade Use prompt audits, instruction layers, and migration evals to retain real requirements, reassess model workarounds, and enforce hard limits in code.
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AI Agent engineering reading list, Sep 25–26, 2026 Seven updates on token efficiency, Codex networking, Copilot Memory, local agent server security, self-practice, skills, and reproducibility.
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AI agent engineering reading list | 2026-09-24 to 09-25 Five updates on proactive safety intervention timing, state revision, TeamHarness, enterprise MCP permissions, and tool-result handling.
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Agent markets: 85% of the loss comes before negotiation Anthropic's book market experiment locates most efficiency loss in preference representation. A look at model choice, preference evals, and market rules.
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GitHub Security Lab makes fuzzing an agent feedback loop GitHub Security Lab Fuzzing Taskflow uses coverage gaps to guide model decisions while deterministic tools execute, measure, and persist results.
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AI agent engineering reading list | 2026-09-23 GitHub Copilot local sandboxing, Agensh, CliffCompaction, SWE-Serve, and enterprise OpenTelemetry support in the Copilot app.
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AI agent engineering reading list | 2026-09-22 Claude Opus 5.5 agent API compatibility, RRSI controls for harness overfitting, Harness-Zero behavior distillation, and Strands harness defaults.
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A Minecraft agent separates planning from fast decisions This Minecraft agent uses Astra for slow planning, JEV for frequent action selection, and Mineflayer for execution, with logs and scripts to verify runs.