How to build your first AI agent in an afternoon
↻Revised 2 times — last on 2025.10.03· open
- 2025.06.12Swapped the example away from naïve function calling toward LangGraph — after a client project made it clear the cyclic-graph model is worth the up-front cognitive cost.
- 2025.10.03Softened the 'no sophisticated RAG' advice. After six more production builds I think a tiny retrieval step in v1 is fine if you keep it honest about its limits.
Building AI agents doesn't have to be a multi-month engineering project. In fact, if you approach it pragmatically, you can build a highly effective, production-ready agent in a single afternoon.
The key is avoiding the hype and focusing on simple, reliable workflows using established frameworks.
1. Start with the "Why"
Before writing a single line of code or crafting a prompt, you need a clear definition of what you are automating. Ask yourself:
- What is the manual task?
- Where is the data coming from?
- What is the expected output?
If your answer includes "It should figure things out autonomously," you're setting yourself up for failure. Agents need guardrails.
2. Choosing the Right Abstractions
While standard Python scripts can orchestrate LLM calls, using a framework like LangGraph introduces state management and cyclic graphs conceptually aligned with how humans think about processes:
from langgraph.graph import StateGraph, END
# Define your state
class AgentState(TypedDict):
input: str
messages: list
# Initialize the workflow
workflow = StateGraph(AgentState)
# Add nodes and edges
# ...
3. The Power of Iteration
Don't worry about sophisticated RAG pipelines in v1. Start by getting the exact prompt structures working reliably against a fixed context block.
"A working agent with hardcoded data is infinitely more valuable than an architectural diagram of a system that hasn't been built yet."
Once your baseline is solid, start expanding its capabilities. This iterative approach is what differentiates successful enterprise deployments from perpetual proofs-of-concept.