LLM Agents: Building AI Systems That Can Think and Act#

LLM Agents are AI systems that can define and execute their own workflows to accomplish tasks. Unlike simple prompt-response patterns, agents make multiple LLM calls, use tools, and adapt their approach based on intermediate results. They represent a significant step toward more autonomous AI systems.

Building LLM Agents with LangChain (13 min)

What Makes an Agent?#

An LLM agent consists of three core components:

  1. LLM Brain: Makes decisions about what to do next
  2. Tools: External capabilities the agent can use (e.g., web search, code execution)
  3. Memory: Retains context across multiple steps

Agents operate through a loop:

  • Observe the environment
  • Think about what to do
  • Take action using tools
  • Observe results
  • Repeat until task completion

Command-Line Agent Example#

We’ve created a minimal command-line agent called llm-cmd-agent.py that:

  1. Takes a task description from the command line
  2. Generates code to accomplish the task
  3. Automatically extracts and executes the code
  4. Passes the results back to the LLM
  5. Provides a final answer or tries again if the execution fails

Here’s how it works:

uv run llm-cmd-agent.py "list all Python files under the current directory, recursively, by size"
uv run llm-cmd-agent.py "convert the largest Markdown file to HTML"

The agent will:

  1. Generate a shell script to list files with their sizes
  2. Execute the script in a subprocess
  3. Capture the output (stdout and stderr)
  4. Pass the output back to the LLM for interpretation
  5. Present a final answer to the user

Under the hood, the agent follows this workflow:

  1. Initial prompt to generate a shell script
  2. Code extraction from the LLM response
  3. Code execution in a subprocess
  4. Result interpretation by the LLM
  5. Error handling and retry logic if needed

This demonstrates the core agent loop of:

  • Planning (generating code)
  • Execution (running the code)
  • Reflection (interpreting results)
  • Adaptation (fixing errors if needed)

Agent Architectures#

Different agent architectures exist for different use cases:

  1. ReAct (Reasoning + Acting): Interleaves reasoning steps with actions
  2. Reflexion: Adds self-reflection to improve reasoning
  3. MRKL (Modular Reasoning, Knowledge and Language): Combines neural and symbolic modules
  4. Plan-and-Execute: Creates a plan first, then executes steps

Real-World Applications#

LLM agents can be applied to various domains:

  1. Research assistants that search, summarize, and synthesize information
  2. Coding assistants that write, debug, and explain code
  3. Data analysis agents that clean, visualize, and interpret data
  4. Customer service agents that handle queries and perform actions
  5. Personal assistants that manage schedules, emails, and tasks

Project Ideas#

Here are some practical agent projects you could build:

  1. Study buddy agent: Helps create flashcards, generates practice questions, and explains concepts
  2. Job application assistant: Searches job listings, tailors resumes, and prepares interview responses
  3. Personal finance agent: Categorizes expenses, suggests budgets, and identifies savings opportunities
  4. Health and fitness coach: Creates workout plans, tracks nutrition, and provides motivation
  5. Course project helper: Breaks down assignments, suggests resources, and reviews work

Best Practices#

  1. Clear instructions: Define the agent’s capabilities and limitations
  2. Effective tool design: Create tools that are specific and reliable
  3. Robust error handling: Agents should recover gracefully from failures
  4. Memory management: Balance context retention with token efficiency
  5. User feedback: Allow users to correct or guide the agent

Limitations and Challenges#

Current LLM agents face several challenges:

  1. Hallucination: Agents may generate false information or tool calls
  2. Planning limitations: Complex tasks require better planning capabilities
  3. Tool integration complexity: Each new tool adds implementation overhead
  4. Context window constraints: Limited memory for long-running tasks
  5. Security concerns: Tool access requires careful permission management
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