AI agents that operate inside your stack.
Multi-step agents that read tickets, query your systems, coordinate tools, and take action — with the guardrails, evaluations, and observability required to run in production.
Who needs this
Signals that agents are the right answer.
Not every problem needs an agent. These are the patterns where agentic systems consistently deliver outsized value.
- A manual workflow that moves between three or more systems.
- Decisions that follow patterns but still need human judgment in edge cases.
- Backlogged operational work that grows faster than headcount can.
- Knowledge workers spending hours on repetitive lookups, triage, or routing.
Example scenarios
Where agents pay for themselves.
Customer operations agents
Agents that classify tickets, pull context from CRM and knowledge bases, draft responses, and escalate when a human is needed.
Back-office automation
Agents that read documents, extract structured data, validate against policy, and post updates to downstream systems.
Developer productivity agents
Internal agents that handle PR triage, flaky-test detection, incident runbook execution, and documentation upkeep.
Knowledge worker copilots
Role-specific copilots that pull from internal data, draft work product, and coordinate across tools.
Our approach
How we build agents that work past the demo.
Evaluated, not just prompted
Every agent ships with an evaluation harness against your real data. We measure, tune, and regression-test — so quality is a number, not an opinion.
Human-in-the-loop where it matters
Agents escalate to humans for ambiguous or high-stakes decisions. We design the handoff deliberately — no silent failures, no runaway automation.
Observability by default
Every action, tool call, and decision is logged and traceable. When something goes wrong, you know exactly what happened and where.
Related
Solutions behind this use case.
Have a workflow worth automating?
Describe the workflow. We'll tell you if AI agents are the right tool.