Building AI agents that actually ship
From prototype to something your team can run every day.
An agent demo is a conversation. A production agent is a system: it has inputs, tools, memory boundaries, failure modes, and owners. The gap between the two is where most projects stall — not because the model is “bad,” but because the product definition skipped engineering reality.
Start with the job, not the persona
We write a short job spec: what triggers the agent, what “done” means, what tools it may call, and what it must never do. That spec becomes the backbone for tests — both automated checks and human review rubrics.
Guardrails are features
Rate limits, allowlisted actions, confirmation steps for irreversible operations, and structured outputs are not optional polish. They are how you keep an agent aligned when users push edge cases. We implement them early so we are not retrofitting safety onto a fragile prompt stack.
Evaluation beats vibes
We maintain a small, evolving dataset of real scenarios (including tricky ones) and measure regressions when models or prompts change. The goal is not perfection on day one — it is visibility: you should know when quality moves and why.
Integrate like any other service
Agents should sit behind the same monitoring, logging, and deployment practices as the rest of your stack. That is how support teams debug issues and how you earn trust from security and compliance stakeholders.
If you are exploring agents for internal workflows or customer-facing assistance, reach out — we will help you scope something shippable, not just interesting.