Reliable agents learning hub
AI Agents, Reliability, and Safety
An AI agent is a controlled system around a model, not an autonomous model by itself. Reliable agents need typed tools, bounded loops, explicit state, least-privilege authority, verification, trace evidence, terminal conditions, and recovery from partial or ambiguous effects.
Best for: Engineers designing tool-using agents and production automation
Recommended learning order
- 1Agentic AIReasoning loops, tools, and multi-agent systems.Open lesson
- 2Agent State, Memory & OrchestrationState graphs, checkpoints, working and semantic memory, durable workflows, interrupts, and multi-agent routing.Open lesson
- 3Model Context Protocol (MCP)Clients, servers, tools, resources, prompts, transports, capability negotiation, authorization, and safe execution.Open lesson
- 4Loop EngineeringBounded state machines, termination contracts, recovery, idempotency, budgets, no-progress detection, and replayable agent loops.Open lesson
- 5Harness Engineering & SandboxesReproducible execution, containers and microVMs, resource limits, mocks, structured observations, and trace capture.Open lesson
- 6AI Evaluation & Quality GatesGolden datasets, RAG and agent metrics, offline and online evaluation, regression tests, and CI release gates.Open lesson
- 7Agent Guardrails & SecuritySchema controls, prompt-injection defense, least privilege, circuit breakers, budgets, approvals, and incident response.Open lesson
Decisions this path should help you make
- Which steps should remain deterministic?
- What authority does each tool require?
- How will the runtime detect no progress or an ambiguous write?
- Which trajectory evidence proves a safe outcome?