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Most AI agents look great in a demo and break in production. This is the AI agent development process I run inside every AI Product Sprint: architecture, eval suites and cost routing, with the real code and YAML from paying engagements.
No forms, no email capture. Step through the phases, read the real snippets, and take the template with you.
The exact config for routing, guards and evaluators I ship inside an AI Product Sprint ($30–60K). Free to download.
The framework from the Production Audit ($3,500): the five boring failures most failing agents have in common.
The routing logic behind a recent client’s LLM bill going from $14K to $5K a month in nine days.
Before writing a single line of LangGraph code, we define exactly what the agent must do, what it must never do, and how we will measure success. Most failed agent projects skip this and pay for it in week 4.
# agent_spec.yaml
name: support_triage
in: customer_message
out: { intent, priority, action, confidence }
must_never:
- escalate_to_billing_without_account_id
- reveal_internal_tooling
eval_pass_threshold: 0.92The whole AI agent development process on one screen: what happens, what you get, and what has to be true before the next phase starts.
Before writing a single line of LangGraph code, we define exactly what the agent must do, what it must never do, and how we will measure success.
Single agent? Supervisor + workers? RAG + tools? The wrong pattern is unrecoverable later.
LangGraph nodes, tool wrappers, prompts, retrieval.
Without an eval suite, you cannot tell a good day from a bad day, and every model release breaks something silently.
We ship to production behind a feature flag at 5% traffic.
Once stable, we tighten.
Milestone payments, 90-day exits, no long-term lock-in.
I’ll map your workflow to the 6 phases, flag the high-risk failure modes, and tell you straight which offer fits. If your situation is buy-not-build, I’ll tell you that too.
Book a 30-min scoping call