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Research dossier · version 1 · updated Oct 30, 2026

LLM Agents & Tool Use: State of the Field (October 2026)

Covers LLM agents that plan and call tools: function calling, coding and computer-use agents, multi-agent systems, benchmarks, security and runtime safeguards, as of October 2026. The key takeaway is that headline agent scores are fragile: benchmark leakage and weak tests can change what a reported number means, and interface drift can erode reliability after deployment, so evaluation design matters as much as model choice.

3,336 words49 sources28 evidence-mapped claims10 benchmarks20 timeline events✓ citation-verified Oct 30, 2026

AI-assisted synthesis of the cited literature, independently fact-checked against its sources; not peer reviewed.

Contents

  1. Executive summary
  2. Background and core concepts
  3. Timeline of key milestones
  4. State of the art
  5. Tool calling and API use
  6. Coding agents and software engineering
  7. Computer-use and web agents
  8. Multi-agent systems
  9. Runtime safeguards and monitoring
  10. Quantitative benchmarks
  11. Open problems and active debates
  12. Major programs and players
  13. Outlook: what to watch (next 12-18 months)
  14. Key papers
  15. Glossary

Sample findings

  • ReAct reports absolute success-rate gains of 34 and 10 points over imitation and reinforcement learning baselines, respectively, on ALFWorld and WebShop.
  • Toolformer learns when and how to call external tools from self-generated supervision.
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