Our process - How we work
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.
Discover
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.
Included in this phase
- Task & workflow mapping
- Tools & data inventory
- Use-case & feasibility check
- Eval criteria defined
- Guardrail & risk review
- Prototype agent (if needed)
- Written brief with costs
Build
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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Deliver
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.
Included in this phase
- Production launch. Deployment, environment and secrets config, guardrails and rate limits live, with go-live support and zero-downtime rollout where applicable.
- Evals & observability. Tracing of tool calls, cost, latency, and output quality, plus the graded eval suite running on a schedule — alerting on regressions before users notice.
- Documentation & handoff. Agent architecture, runbooks for common failure modes, the eval suite, and a live walkthrough with your team before we step back.
Our values - How we think about building agents
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.
- Evals before features. We measure an agent before we trust it. A graded set of real tasks defines "good" up front, so every change is judged on evidence — not a single impressive run.
- Honest scoping. We tell you where an LLM genuinely helps and where it does not, what is risky, and what we are uncertain about — with all costs upfront. Reaching for AI where simpler code wins is a failure, not a feature.
- Guardrails from day one. Input validation, output checks, tool permissions, rate limits, and failure modes are designed in from the first commit. Retrofitting safety onto an agent already taking actions is far harder than building it in.
- Human in the loop where it matters. Agents should act autonomously on low-risk steps and pause for a human on high-stakes ones. We put the checkpoints exactly where a wrong action would be expensive — no more, no less.
- Right tool, right job. We do not reach for an agent where a simple rule or deterministic code is faster, cheaper, and more reliable — and we do not avoid AI when it genuinely solves the problem. The goal is the right outcome.
- Observable & legible. You can see what the agent retrieved, decided, and did — and so can your team. Traceable behavior and clear documentation are not optional extras; they are how you keep trusting it.
Tell us about your system
Our offices
- San Diego
450 S Melrose Dr Ste. 107,
Vista, CA 92081, USA
(800) 277-9389