Production agentic systems,
not pilots.
Most federal AI never leaves the demo. We ship software that survives the mission, the data, and the accreditor — built the way operators build, because we are operators.
Four phases, one running system.
The method moves from the mission workflow to production code to operator ownership. Each phase produces something you can use — not a deck that recommends the next phase.
Discover
We map the mission workflow with the people who run it, then rank the opportunities by value and feasibility. You leave Discover knowing which work an agent should touch first, where the data lives, and what would have to be true for the system to be trusted.
Design
We design the agent architecture: where the loop runs, which tools it can call, the integration points into your systems of record, the human-in-the-loop gates, and the security model. The accreditor's questions are answered in the design, not discovered during the review.
Build
We write production code: eval suites that run on every change, version control on prompts and policies, observability on every run, sandboxed tool calls, and a rollback path. The system runs on a duty cycle. A demo that breaks quietly is not a deliverable.
Run & Improve
We monitor the running system, tune it against the eval set, and expand scope as trust grows. Then we transfer ownership — your team operates the system and reasons about agents without us in the room. The goal is your independence, not our retainer.
The chat box is one wire into a much larger machine.
What you've used is an interface. What does the work is a harness — the production engineering wrapped around a reasoning loop. Hover any node to see what we cover.
A system, not a chatbot
A harness is a closed-loop working system around the model. The loop reasons and acts; the harness makes it observable, testable, and safe to run in production. Hover any node — each is something we cover and build.
What separates a system from a demo.
A demo runs once, in front of an audience. A system runs every day, against changing data, with no one watching. The difference is engineering you can point to.
- → Eval suites — golden-set and end-to-end checks that run on every prompt, policy, or model change. Regressions are caught before deploy, not in front of the mission.
- → Observability — every run produces a trace: inputs, outputs, retries, failures, cost, and latency, searchable and shareable.
- → Version control — prompts, policies, and tool definitions are versioned like code. Every decision is traceable to the version that made it.
- → Rollback — a known-good path back. When a change misbehaves, you revert in minutes, not incident reports.
Security is infrastructure, not a feature.
The accreditor is part of the design conversation. Every agent runs inside a security model that an auditor can inspect — and that holds up when the enclave is disconnected from the outside world.
- → Tamper-evident audit — every decision logged and chained, traceable to the policy version behind it.
- → PII redaction and input validation on the way in; output filtering on the way out.
- → Scoped credentials and request signing — agents act with least privilege, and every call is attributable.
- → Token and cost budgets — hard ceilings that stop a runaway loop before it becomes a bill or an incident.
- → Air-gapped deployment — a vendored harness, local NER, and Ollama or vLLM serving mean classified and disconnected enclaves run with zero external calls.
See the method against your mission.
Bring a workflow. We will map where an agent belongs, what the security model has to cover, and how fast a working system reaches production.