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AI Agents & Autonomy

Systems that plan and take actions toward goals — using tools, memory, and (often) other agents.

AI Agents & Autonomy is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart TB
  G[/Goal/] --> PL[Plan] --> ACT[Act with tools] --> OBS[Observe]
  OBS --> Q{Goal met?}
  Q -- no --> PL
  Q -- yes --> DONE[/Result/]
  MEM[(Memory)] -.-> PL

Key topics

  • Agent architectures

    Perceive–reason–act loops, ReAct, and planner/executor designs.

  • Tool use & function calling

    Letting models call APIs, run code, search, and act in the world.

  • Planning & reasoning

    Decomposing goals into steps; reflection and self-correction.

  • Memory

    Short- and long-term memory, and retrieval to persist context across steps.

  • Multi-agent systems

    Orchestrating specialised agents that collaborate or debate.

  • Orchestration & safety

    Guardrails, sandboxing, and human-in-the-loop for autonomous systems.

What makes something an agent

A plain LLM answers a question. An agent pursues a goal — it can decide to take actions (call tools, run code, search the web), observe the results, and adjust. The defining feature is a loop with feedback, not one-shot output.

flowchart LR
  GOAL[/Goal/] --> PLAN[Plan / decide next step]
  PLAN --> ACT[Act: call a tool]
  ACT --> OBS[Observe result]
  OBS --> DONE{Goal met?}
  DONE -->|no| PLAN
  DONE -->|yes| OUT[/Result/]

Where agents break

Agents are powerful but fragile. The main failure modes are worth knowing before you build one:

  • Error compounding — a small mistake early in a long loop cascades. A 95%-reliable step run 20 times succeeds end-to-end only ~36% of the time.
  • No ground truth — without good evaluation, you can't tell a working agent from a lucky one.
  • Tool misuse — calling the wrong tool, or the right tool with bad arguments.
  • Security — untrusted content can hijack an agent via prompt injection (see Privacy & Security in AI).

Design principle

Keep loops short, give agents fewer, well-described tools, and verify their work. Reliability comes from constraint, not from cleverness.

NLP & Large Language Models · Reinforcement Learning · Building with AI · AI Safety, Alignment & Ethics

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