For RevOps, support, and engineering leaders who've seen the demos and want the real thing. We design, build, and harden agents that operate inside your systems, with evals, guardrails, and observability built in from day one.
Most "AI projects" stop at a chat box. We build agents that take actions, file the ticket, draft the deal review, reconcile the records, escalate when uncertain. They run inside your stack, with the same access controls and audit trails as a human teammate, and they get measurably better every week.
For leaders who've watched a slick demo and want to know what it takes to actually ship one, safely, into a system of record.
A demo is a transcript. A production agent is a teammate, with permissions, evaluations, and a manager.
An agent program isn't a model call, it's a stack. The eight pieces below are what separate a Friday demo from a worker that runs on Monday and is still running in Q4.
Pick the work the agent owns vs. the work it escalates. The job description before the build.
The functions the agent can actually call, scoped, typed, idempotent, and revocable.
A test set you trust. Every change runs against it. Regressions blocked before they reach prod.
Input validation, output checks, refusal patterns. The agent says "I don't know" before it hallucinates.
Retrieval, scratchpads, and session state, sized to the task, not maxed out by default.
Every trace, prompt, and tool call logged. Cost, latency, and quality on one dashboard.
When the agent isn't sure, who picks it up? Real human handoff, not a dead-end form.
Access scoping, PII boundaries, model versioning, audit trails. Compliance won't be the blocker.
Pick a real task on the left and watch what actually happens. The model is one of seven layers, and most of the work that makes an agent reliable lives in the other six.
get_order(id) → check_eligibility() → issue_refund()
action
Five phases. Five working agreements. We ship a real agent into a real system, small at first, broader as the eval set proves it out.
Pick the task. Define what counts as success. Write a 1-page job description for the agent.
Wire the model to a thin tool layer. Hit it with 20 real cases. Iterate fast on prompt + tools.
Build the eval set. Add guardrails. Test the failure modes you don't want explained on a Monday.
Roll out to a single team or queue. Observability dashboard live. Escalation paths wired to humans.
Weekly eval review. Cost tuning. Expand scope as confidence grows. New tasks queued every month.
The left column is what most agent projects look like at week six. The right is what we ship, small surface area, high reliability, observable end-to-end.
A wrapper around a model with no tools, no memory, no policy. It hallucinates on edge cases and never takes an action.
Tool-calling, retrieval, evaluated on real cases. Makes decisions, files tickets, escalates when uncertain.
72% tasks auto-resolved"Looks good, ship it." No baseline, no regression test. Every prompt change is a coin flip and the team is afraid to touch it.
200+ frozen test cases. Every prompt or model swap runs against them. Regressions blocked at the door.
94% pass rate · 200 casesYou can't tell why the agent did what it did. Cost is a surprise on the monthly bill. Latency is whatever it is.
Every prompt, tool call, and token logged. Cost, latency, and quality on one dashboard.
MTTD < 5 min · per-trace costNo guardrails. The agent confidently makes up policy, prices, and account details. PII flows wherever the prompt goes.
Output checks, refusal patterns, scoped permissions. The agent says "I don't know" before it makes things up.
0 PII leaks · scoped toolsYou shipped on one model. The vendor changes pricing or deprecates the API and your stack breaks overnight.
Agent logic in your code, not the vendor's console. Swap providers in a sprint, not a re-platform.
Any model · same harnessRecognize three or more? Book the 10-minute intro call →
Four phases. Each one starts with a real task and ends with something your team can operate, evaluate, and improve without us.
We pick the task, define what "done" means, and write a one-page job description for the agent. What it owns, what it escalates, what counts as a win, agreed before we touch a model.
Wire the model to a thin tool layer. Run it against twenty real cases, the messy ones, not the demo-friendly ones. Iterate the prompt, the tools, and the retrieval together until the loop is stable.
Build a real eval set from 200+ historical cases. Add output guardrails. Tune for cost. Stress-test the failure modes you don't want explained on a Monday call. Every change runs the suite.
Roll out to a single team or queue first. Observability dashboard live before traffic. Escalation paths wired to real humans. Weekly eval reviews after, improvements compound month over month.
Four phases · one production agent · zero hand-waving
If you don't see yours here, the easiest way to get an answer is to ask. We don't gate calls.
Email us a question →Anthropic Claude, OpenAI, and BYOM setups on your own infra. We pick the model that gets your eval set across the line at the cost and latency you can afford to run forever, not the one with the loudest launch event.
High-volume, well-bounded, and reversible. The work that's done the same way thousands of times a quarter, where a wrong answer can be caught and corrected. Big, ambiguous decisions stay with humans.
Every action is traced. Confidence-scored decisions either auto-resolve, ask for confirmation, or escalate to a human queue with full context. Mistakes are visible inside an hour, not a month.
Yes, we work to it, not around it. Scoped service accounts, encrypted secrets, PII redaction at the boundary, audit trails on every action. Your security team reviews the same tool layer the agent does.
No. Agent logic, eval suite, and tool layer live in your code, not a vendor console. Swap models in a sprint without rewriting the agent. We've done it; it works.
Most agent programs pull in one or two of these. Workflow automation builds the rails the agent runs on, data makes its decisions trustworthy, and strategy decides which agents are worth shipping at all.
Frame the bet, platform mix, operating model, and roadmap before the build queue.
→Production agents with tools, evals, guardrails, observability, and human handoff.
●Capture, dedupe, score, route, and nurture without leaks between marketing and sales.
→Behavior-triggered programs that ship at scale across lifecycle, nurture, and expansion.
→Schemas, cleanup, enrichment, quality monitoring, and governance.
→Integrations and back-office workflows that remove manual handoffs.
→A demo doesn't scale. A production agent, small, evaluated, observable, does. Bring us a real task and we'll tell you in thirty minutes whether it's a fit.