Our 6-Week Framework
We've built 12+ AI agents in production across healthcare, logistics, fintech, and e-commerce. Across every one, the recipe rhymes: clear scope, robust tools, ruthless evals, and a thoughtful human-in-the-loop strategy.
Week 1–2 · Discovery & Architecture
We map the workflow your agent will own. Every step. Every decision. Every fallback. We document success metrics that an executive could read — accuracy, deflection, cost-per-resolution — and we lock the agent graph before any code is written.
Week 3–4 · Core Agent Development
Tools first, prompts second. We model the agent's "hands" — APIs, database access, file operations — and only then design how the LLM should orchestrate them. We use LangGraph or a custom state machine for non-trivial flows, and we keep all prompts in a version-controlled folder with diffs and reviews.
Week 5 · Testing & Hardening
Eval suites, red-team prompts, jailbreak resistance, structured-output validation, and graceful fallbacks. We instrument cost, latency, and accuracy per node. We build the "boring" parts: rate limiting, idempotency, and audit logs.
Week 6 · Deployment & Monitoring
A phased rollout — 5%, 25%, 100% — gated by live dashboards. We staff a war room for the first 72 hours and tune the prompts based on real traffic. Then we hand over a runbook your team can operate.
Real Example: Invoice Processing Agent
We built one for a logistics company that now processes 4,200 invoices a week. It reads PDFs, validates against the PO, flags discrepancies, and posts to the ERP. Accuracy: 97.3%. Cost: ~$0.07 per invoice. ROI: 7 months.
Common Mistakes to Avoid
- Designing prompts before tools
- No eval suite (you'll regret it on day 30)
- Treating the agent like a chatbot
- Skipping human-in-the-loop ramp
Ready to Build Your AI Agent?
Talk to us about a 6-week build. We'll scope it in two calls and ship something measurable.