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Shipping an agent you can trust in production takes more than a clever prompt. We move through three phases — Discover, Build, and Deliver — with evals, guardrails, and human-in-the-loop checkpoints at every step.
Before any prompt is written, we map the task the agent should own — the steps a person takes today, the tools and data it needs to reach, the actions it can safely take, and the decisions that must stay with a human. We talk to the people who do that work, not just the stakeholders who describe it.
We pressure-test where an agent genuinely adds value versus where deterministic code or a simple rule is faster, cheaper, and safer. Not every problem needs an LLM, and we will tell you when it does not.
The output is a written brief: the workflow being automated, the data and tools to wire in, the guardrails and human-in-the-loop points, the way we will measure success (the evals), and an honest read on complexity, cost, and risk. We advise all costs upfront. No surprises later.
We define the agent contract first — its goal, the tools and actions it can call, the guardrails on each one, and the success criteria — before wiring anything to production. Retrieval over your private data, structured outputs, and tool orchestration are designed deliberately, not bolted on.
We build the eval harness alongside the agent, not after. A graded set of real tasks tells us whether a change actually improves behavior or just looks better in one demo — so we iterate against evidence, not vibes.
Development is iterative and visible: you see the agent running in a staging environment early, with traces of what it retrieved, decided, and did. Human-in-the-loop checkpoints go in at the steps where a wrong action would be expensive.
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Launch is where production reality starts. We ship with observability for the things agents actually fail on — tool-call errors, hallucinations, runaway cost, latency, and output quality — wired to alerting before go-live, with guardrails and rate limits live in production.
Evals keep running after launch. We monitor agent behavior against the graded task set over time and catch regressions when a model update or a data change shifts quality — before your users feel it.
We do a structured handoff: documentation, runbooks for common failure modes, the eval suite, and a working knowledge of how the agent fits together. For teams who want ongoing support we offer retainers for tuning, new capabilities, and model upgrades; for teams who want to own it, we make sure they can.
The decisions that determine whether an agent is safe to trust in production are not the exciting ones — they are the evals, guardrails, and honest tradeoffs most demos skip.
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We map your workflows, data, and tools, then hand you a prioritized plan of the agentic-AI automations that save the most time and money — costs upfront, no obligation.