AI Agents & Autonomous Systems

AI agents are autonomous systems that perceive, decide, and act to achieve goals. Using LLMs as reasoning engines, modern agents plan, use tools, and adapt - achieving 80-90% task completion on complex multi-step problems.

What are AI Agents?

• Autonomy: Self-directed, not just responding to prompts
• Planning: Break down goals into steps
• Tool Use: Call APIs, databases, search engines
• Memory: Remember past interactions and learnings
• Adaptation: Learn from feedback, retry on failure

Agent Frameworks

1. LangChain Agents

• Most popular agent framework
• Built-in tools: search, calculator, SQL, APIs
• ReAct (Reasoning + Acting) pattern
• Easy integration with LLMs (GPT-4, Claude)

2. AutoGPT

• Fully autonomous goal-driven agent
• Self-prompts to plan and execute
• Memory and long-term planning
• Experimental, pushing boundaries

3. BabyAGI

• Task-driven autonomous agent
• Creates, prioritizes, executes tasks
• Simple but powerful

4. Agent Protocols (OpenAI Assistants API)

• Managed agents from LLM providers
• Built-in tools: code interpreter, retrieval
• Persistent memory

Agent Components

1. LLM (Brain)

• GPT-4, Claude for reasoning and planning
• Decides what action to take next

2. Tools

• Search: Google, Wikipedia, custom knowledge bases
• Computation: calculator, code execution
• Data: SQL queries, APIs, web scraping
• Actions: send email, make API call, file operations

3. Memory

• Short-term: Current conversation context
• Long-term: Vector database for past interactions
• Enables learning and personalization

4. Planning

• Chain-of-thought: Step-by-step reasoning
• ReAct: Reason about action, then act
• Self-reflection: Evaluate own outputs

Applications

• Research Assistants: Gather info from multiple sources
• Customer Service: Handle complex multi-step requests
• Data Analysis: Query databases, generate insights
• Code Generation: Write, test, debug code autonomously
• Workflow Automation: Execute business processes

Challenges

• Reliability: 80-90% success vs 95%+ needed for critical tasks
• Cost: Many LLM calls can be expensive
• Safety: Need guardrails to prevent harm
• Hallucination: LLMs can generate false info

Best Practices

• Start with constrained agents (limited tools/actions)
• Human-in-the-loop for critical decisions
• Implement safety checks and rollbacks
• Monitor agent behavior and outcomes
• Use GPT-4 or Claude for best reasoning