---
id: autonomous-agents
name: "Autonomous Agents"
domain: ML
skill: faion-ml-engineer
category: "ml-engineering"
version: "2.0"
updated: "2026-01"
---

# Autonomous Agents

## Overview

Autonomous agents are LLM-powered systems that independently plan, execute tasks, use tools, and iterate toward goals. They combine reasoning, memory, and action capabilities to accomplish complex objectives with minimal human intervention.

**Market context (2025-2026):** The AI agents market grew from $5.4B (2024) to $7.6B (2025), projected to reach $50.3B by 2030 (45.8% CAGR). Gartner reported 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025.

## When to Use

| Use Case | Recommended Pattern |
|----------|-------------------|
| Dynamic, exploratory tasks | ReAct |
| Complex, high-stakes operations | Plan-and-Execute |
| Tasks requiring iteration | Reflexion |
| Research and analysis | Multi-agent |
| Code generation/execution | ReAct + tools |
| Knowledge work automation | Hybrid |

## Architecture Models

### Three Core Models

| Model | Description | Use When |
|-------|-------------|----------|
| **Reactive** | Stimulus-response, no planning | Simple, well-defined tasks |
| **Deliberative** | Full planning before action | Complex, multi-step tasks |
| **Hybrid** | Plan globally, act locally | Production systems |

### Agent Architecture Components

```
Agent System
├── Memory Layer
│   ├── Short-term (conversation context)
│   ├── Long-term (vector DB, persistent)
│   └── Episodic (task experiences)
│
├── Planning Layer
│   ├── Task decomposition
│   ├── Dependency resolution
│   └── Strategy selection
│
├── Reasoning Layer (LLM)
│   ├── Chain-of-thought
│   ├── Self-reflection
│   └── Decision making
│
├── Tool Layer
│   ├── Tool selection
│   ├── Parameter extraction
│   └── Result processing
│
└── Execution Layer
    ├── Action dispatch
    ├── Error handling
    └── State management
```

## Agent Patterns

### Single-Agent Patterns

| Pattern | Description | Strengths | Weaknesses |
|---------|-------------|-----------|------------|
| **ReAct** | Reason + Act loop | Adaptive, simple | May lose focus |
| **Plan-and-Execute** | Plan first, execute | Governance, control | Less adaptive |
| **Reflexion** | Self-critique, retry | Learning, improvement | Higher latency |
| **Tree-of-Thought** | Explore multiple paths | Better reasoning | Expensive |
| **MCTS** | Monte Carlo Tree Search | Optimal decisions | Very expensive |

### Multi-Agent Patterns

| Pattern | Description | Use When |
|---------|-------------|----------|
| **Supervisor Crew** | Orchestrator + specialist agents | Task decomposition |
| **Hierarchical Teams** | Multi-level orchestration | Complex workflows |
| **Debate** | Agents argue, reach consensus | Validation, quality |
| **Market-Based** | Agents bid for tasks | Resource optimization |

## ReAct vs Plan-and-Execute

| Aspect | ReAct | Plan-and-Execute |
|--------|-------|------------------|
| **Timing** | Interleaved reasoning/action | Separate plan/execute phases |
| **Adaptability** | High (adjusts each step) | Lower (follows plan) |
| **Control** | Less structured | High governance |
| **Cost** | Often 50% cheaper | More LLM calls |
| **Best for** | Exploratory tasks | High-stakes operations |

**Recommendation:** Hybrid models that "plan globally and act locally" provide the best balance for production systems.

## Framework Selection (2025-2026)

| Framework | Best For | Key Features |
|-----------|----------|--------------|
| **LangGraph** | Production workflows | State machines, persistence |
| **CrewAI** | Multi-agent teams | Role-based, collaboration |
| **AutoGen** | Research, complex agents | Conversational agents |
| **LlamaIndex** | Data-heavy agents | RAG integration |
| **DSPy** | Optimized prompts | Automatic optimization |
| **Haystack** | Search-focused | Pipeline architecture |

## Files in This Module

| File | Purpose |
|------|---------|
| [README.md](README.md) | Overview (this file) |
| [checklist.md](checklist.md) | Implementation checklist |
| [examples.md](examples.md) | Code examples |
| [templates.md](templates.md) | Production templates |
| [llm-prompts.md](llm-prompts.md) | System prompts |

## Best Practices

1. **Clear Goal Definition** - Specific, measurable objectives with bounded scope
2. **Tool Design** - Clear descriptions, proper error handling, appropriate granularity
3. **Safety** - Iteration limits, sandboxed execution, human approval for critical actions
4. **Monitoring** - Log all actions, track token usage, alert on failures
5. **Memory Management** - Summarize long histories, use relevance filtering

## Common Pitfalls

| Pitfall | Solution |
|---------|----------|
| Infinite loops | Set iteration limits |
| Tool abuse | Rate limiting, cooldowns |
| Context overflow | Summarization, pruning |
| Hallucinated actions | Grounding, verification |
| No guardrails | Safety constraints |
| Poor error recovery | Retry logic, fallbacks |

## References

- [ReAct Paper](https://arxiv.org/abs/2210.03629) - Original ReAct architecture
- [Reflexion Paper](https://arxiv.org/abs/2303.11366) - Self-reflection agents
- [LangChain Agents](https://python.langchain.com/docs/modules/agents/)
- [LangGraph](https://langchain-ai.github.io/langgraph/)
- [CrewAI](https://www.crewai.com/)


## Agent Selection

| Task | Model | Rationale |
|------|-------|----------|
| Autonomous loop implementation | opus | Complex pattern |
| Goal decomposition | sonnet | Planning expertise |
| Safety guardrails | opus | Security design |

## Sources

- [Agentic AI Trends 2026](https://machinelearningmastery.com/7-agentic-ai-trends-to-watch-in-2026/)
- [AI Agent Architectures Guide 2025](https://dev.to/sohail-akbar/the-ultimate-guide-to-ai-agent-architectures-in-2025-2j1c)
- [ReAct vs Plan-and-Execute](https://byaiteam.com/blog/2025/12/09/ai-agent-planning-react-vs-plan-and-execute-for-reliability/)
- [Agentic Design Patterns](https://www.marktechpost.com/2025/10/12/5-most-popular-agentic-ai-design-patterns-every-ai-engineer-should-know/)
- [AI Agent Architecture 2026](https://www.lindy.ai/blog/ai-agent-architecture)
