Agents that take real action in your systems and know when to stop.
Multi-step agents that read your tools, call them safely, and roll back when they're unsure. Reliability is the product.
- Typical duration
- 10–16 weeks
- Squad
- 3–5 senior engineers
- Starting at
- $32,000 / month
- Tasks completed without escalation
- 62%Tasks completed without escalation
- Reversible action coverage
- 100%Reversible action coverage
- Mean steps per completed task
- 7.4Mean steps per completed task
An agent that acts is only as good as its ability to not act.
Demo agents chain tool calls impressively and fail silently in production. The engineering that matters is the unglamorous part: knowing when confidence is too low to proceed, making every side effect reversible, and leaving a trace an auditor can replay. Autonomy without those three things is a liability.
Sound familiar?
- Agent prototypes nobody will authorize to touch production systems
- Failures that can't be reproduced or explained after the fact
- No policy layer between the model and a destructive action
- Cost and latency that spiral on multi-step tasks
How we deliver ai agents.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Scope the authority
We write down exactly what the agent may do, what requires approval, and what it must never attempt. This document precedes the code.
Build the tool layer
Every tool gets a strict schema, idempotency guarantees, and a dry-run mode. The agent is only ever as safe as its worst tool.
Add checkpoints
Deterministic gates between reasoning steps — confidence thresholds, policy checks, and human approval where the blast radius warrants it.
Instrument & evaluate
Full replayable traces plus a task-level eval suite, so behavioral regressions surface in CI rather than in a customer escalation.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
Tool-use protocols
Strictly typed tool schemas with validation, retries, and idempotency keys — so a repeated call never double-charges or double-writes.
Deterministic checkpoints
Policy gates between steps that halt the agent on low confidence, out-of-policy actions, or anything above a configured blast radius.
Replayable traces
Every run recorded end-to-end — prompts, tool calls, intermediate state — and replayable against a new model version to compare behavior.
Multi-agent orchestration
Planner/worker topologies with explicit handoffs and shared state, used only where a single agent genuinely cannot hold the task.
Approval workflows
Human sign-off surfaces in Slack, email, or your own console — with the agent's reasoning and evidence attached to the request.
Cost & latency governance
Per-run budgets, step caps, and model routing that spends frontier-model tokens only on the steps that need them.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- Claude
- LangGraph
- OpenAI
- Temporal
- Python
- TypeScript
What this looks like in production.
Cutting prior-auth cycle time by 71%
Northwind Health
- Problem
- Manual prior authorization consumed 6+ hours per case and delayed care for thousands of patients.
- Solution
- We built a HIPAA-compliant agent that drafts letters, attaches evidence, and routes to payers via existing APIs.
- Outcome
- Average cycle time fell from 4.2 days to 1.2 days. Denials dropped 38% in the first quarter post-launch.
- Cycle time reduction
- 71%Cycle time reduction
- Denial reduction
- 38%Denial reduction
- Hours saved / clinician / week
- 14Hours saved / clinician / week
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.