Multi-step AI agents that handle real work end-to-end.
LLM-powered agents that do things — research, draft, route, action — inside controlled boundaries.
When chatbots aren't enough
You need AI that does work, not just answers questions. Multi-step processes — lead research, ticket triage, content production — that today eat hours of human time.
You've seen agentic demos but you don't trust them in production. Reasonable.
We build agents with explicit guardrails, eval suites, and failure modes designed up front. Production-grade, not demoware.
How we build agents
- 01
Bound the task
Clear inputs, outputs, success criteria, and failure modes. Most agentic problems are scoping problems.
- 02
Build the tool surface
The agent can only do what its tools let it do. Tools are designed before the agent is.
- 03
Evaluate ruthlessly
Eval suite covers happy path and adversarial inputs. Refusal mode is the default for ambiguity.
What good looks like
An agent that takes work off your team's plate consistently — with clear boundaries and rollback paths when it fails.
Reliable in scope
Within its bounded task, the agent succeeds 95%+ of the time.
Safe outside scope
Refuses or escalates rather than guessing.
Auditable
Every decision the agent makes is logged and inspectable.
Proof
8 hours/week saved per ops manager
Lead-research agent for the ops team — runs unattended, surfaces qualified leads with full citations.
— Mid-market services firm
Frequently asked questions
Deterministic automation handles deterministic problems. Agents handle the messy, judgment-required ones — but only within bounds. We use both, picking by problem.
Ready to put AI to work?
Tell us about a process that eats your team's time. We will tell you whether an agent can take it on.