Autonomous Agent Design Patterns
Autonomous agents go beyond single-turn task execution. They decompose goals into plans, execute steps, evaluate results, and adjust their approach when things go wrong.
Autonomous vs Task Agents​
| Aspect | Task Agent | Autonomous Agent |
|---|---|---|
| Scope | Single task | Multi-step goal |
| Planning | None | Decomposes goals into steps |
| Memory | Stateless | Maintains working memory |
| Self-correction | Retry on error | Re-plan on failure |
| Human oversight | Per-invocation | Goal-level approval |
| Risk | Low (bounded output) | Higher (unbounded actions) |
Core Components​
An autonomous agent requires:
- Planner - Breaks a high-level goal into actionable steps
- Executor - Runs each step using tools and LLM calls
- Evaluator - Assesses whether the step achieved its objective
- Memory - Tracks completed steps, intermediate results, and context
- Guardrails - Limits on actions, budget, time, and scope to prevent runaway execution
What This Course Covers​
| Module | Topic |
|---|---|
| 1 | Goal decomposition and planning strategies |
| 2 | ReAct pattern (Reason + Act) implementation |
| 3 | Working memory and scratchpad management |
| 4 | Self-evaluation and re-planning |
| 5 | Tool selection and dynamic tool loading |
| 6 | Guardrails for safe autonomy (budget, time, action limits) |
| 7 | Human-in-the-loop approval gates |
Premium
Autonomous Agent Design Patterns
Get the complete 7-module course with ReAct implementations, planning strategies, and production guardrail configurations.