Agents & Tool Use
Building autonomous AI systems that reason, act, and use tools
Overview
This section covers the design, implementation, and evaluation of LLM-powered agents. Agents extend LLMs beyond text generation to autonomous task completion through reasoning, tool use, and interaction with external environments. This is a rapidly evolving area critical for GenAI engineering interviews.
Learning Objectives
By completing this module, you will be able to:
- Explain the key agent architectures (ReAct, MRKL, Plan-and-Execute) and their trade-offs
- Implement function calling with proper schema design and parameter extraction
- Design multi-agent systems with appropriate communication and orchestration patterns
- Build memory systems for maintaining context and state across interactions
- Evaluate agent performance using task completion, efficiency, and safety metrics
Module Structure
| Module | Focus | Key Concepts |
|---|---|---|
| Agent Architectures | Core reasoning patterns | ReAct, Plan-and-Execute, MRKL, Toolformer |
| Function Calling | Tool integration | Schemas, parameter extraction, OpenAI API |
| Multi-Agent Systems | Collaboration patterns | Orchestration, debate, AutoGen |
| Memory & State | Context management | Short/long-term, episodic, vector stores |
| Agent Frameworks | Implementation tools | LangChain, LlamaIndex, CrewAI comparison |
| Agent Evaluation | Performance measurement | Task success, efficiency, safety metrics |
What Makes Agents Different from LLMs
| Aspect | Standard LLM | LLM Agent |
|---|---|---|
| Interaction | Single turn | Multi-turn with environment |
| Capabilities | Text generation only | Tool use, API calls, code execution |
| Memory | Context window only | Persistent memory systems |
| Autonomy | None | Task decomposition, self-correction |
| Error handling | User must retry | Self-debugging and retry |
Core Agent Loop
Interview Focus Areas
| Topic | Frequency | Question Types |
|---|---|---|
| ReAct Pattern | Very High | Explain, implement, compare |
| Function Calling | Very High | Design schemas, handle errors |
| Multi-Agent Design | High | Architecture, coordination |
| Memory Systems | High | Design, trade-offs |
| Framework Selection | Medium | Compare, justify choices |
| Safety & Evaluation | Medium | Metrics, failure modes |
Quick Reference: Agent Patterns
| Pattern | When to Use | Key Advantage |
|---|---|---|
| ReAct | Single-agent, iterative tasks | Simple, interpretable |
| Plan-and-Execute | Complex, multi-step tasks | Structured planning |
| MRKL | Diverse tool requirements | Modular, extensible |
| Multi-Agent | Specialized subtasks | Expertise distribution |
| Reflexion | Learning from failures | Self-improvement |
Prerequisites
Before diving into agents, ensure familiarity with:
- LLM fundamentals (tokenization, context windows, temperature)
- Prompt engineering (few-shot, chain-of-thought)
- API usage (OpenAI, Anthropic, or similar)
- Basic Python async programming
Recommended Learning Path
- Start with Agent Architectures to understand reasoning patterns
- Master Function Calling for practical tool integration
- Study Multi-Agent Systems for complex coordination
- Learn Memory & State for persistent agents
- Compare Agent Frameworks for implementation choices
- Finish with Agent Evaluation for production readiness
Sources
- Yao et al., "ReAct: Synergizing Reasoning and Acting in Language Models" (2022)
- Schick et al., "Toolformer: Language Models Can Teach Themselves to Use Tools" (2023)
- OpenAI Function Calling Documentation
- LangChain Agent Documentation