This is the technical build week. If you're working with a dev team, they should be building. If you're building yourself, this is where the code gets written.
Day 6–7: Build the tool integrations first
Counter-intuitive advice: don't start with the agent logic. Start with the tools. Write the functions that let the agent interact with each external system. Test each one independently before connecting them to the agent.
A "tool" in LangChain terms is a Python function with a clear name, description, and input/output schema. Each tool should do one thing and do it well:
@tool
def lookup_customer(customer_id: str) -> dict:
"""Look up customer details from CRM by customer ID."""
# API call to CRM
return customer_data
@tool
def update_ticket_status(ticket_id: str, status: str, notes: str) -> bool:
"""Update a support ticket status in the helpdesk system."""
# API call to helpdesk
return success
Test every tool in isolation before you put the agent in the loop. This makes debugging dramatically easier, you know a tool works before you're trying to debug whether the agent is using it correctly.
Day 8–9: Build the agent skeleton
Now connect the tools to a basic agent loop. Start with the happy path only. Get the agent handling the most common version of the process reliably before you add any edge case handling.
Use LangChain's ReAct agent pattern as a starting point. It's well-documented, production-tested, and handles the think-act-observe loop that most business automation agents need.
Your initial system prompt should encode:
- What the agent's role is and what it's responsible for
- The tools available and when to use each
- The output format expected
- Key constraints (what the agent should never do)
Day 10: Add the edge case handling
Now go through your edge case inventory from Week 1. For each edge case:
- Can it be handled by adding logic to the system prompt? (Often yes)
- Does it require a new tool or a new data source?
- Should it route to human review rather than be handled automatically?
Build explicit handling for the top 5 edge cases by frequency. For low-frequency, high-consequence edge cases, build a human-review routing path rather than trying to automate the judgment call.
