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Services · Agentic systems

Ship governed AI agents that actually run your back-office work.

We build multi-step agents that take a task, call your tools, ask for approval at the right moments, and leave a trail your operations and risk teams can audit on Monday morning.

What you get

Three concrete deliverables.

First workflow in 90 days

Production agent with tool surface

Typed tool registry against your systems, deterministic step boundaries, and a state machine the team can read — not an opaque loop.

Shipped with the agent

Approval and intervention console

Approval queues for high-stakes steps, side-by-side context view, and the ability to pause, edit, or override any agent decision in flight.

From day one

Trace and audit plane

Per-step traces, tool-call logging, model and prompt version stamping, and an audit export that satisfies your risk reviewer without a special request.

How we work

From kickoff to production.

012 weeks

Workflow framing and risk tiering

Pick one workflow, decompose it into steps, and tag each step by reversibility. High-risk steps get human approval by design.

023 weeks

Tool surface and state machine

Build typed tools against your systems with explicit success and failure modes. Wire the agent loop as a readable state machine, not a stochastic free-for-all.

035 weeks

Approval console and trace plane

Stand up the human-in-the-loop console, the per-step trace store, and the audit export. Test failure modes in a sandbox before any production traffic.

043 weeks

Shadow, partial, full cutover

Shadow mode against real tickets, then partial traffic with mandatory approval, then full operation with approval thresholds tuned on data.

The stack we build on.

Cloud-agnostic. We meet you where your tenant lives.

Anthropic Claude (tool use)OpenAI AssistantsMicrosoft Copilot StudioLangGraphTemporalPostgresOpenTelemetryKubernetes

Outcome metrics

70%+
Workflow auto-completion

Median after first tuning cycle

100%
Steps with audit trace

By design, not by best effort

4hr
Median ticket resolution

Down from 3 business days

From the field

One we shipped.

Enterprise audit operations

Replaced a manual evidence-fetching workflow with an agent that pulls source artifacts, validates against the control language, and routes exceptions to the reviewer queue — same auditors, four times the throughput.

4x

Reviewer throughput

First quarter of operation

Read the case study

FAQ

Questions buyers ask first.

How do you keep the agent from doing something destructive?
Every tool is typed with an explicit risk tier. Reversible reads run autonomously; irreversible writes require human approval; spend-bearing tools have hard ceilings enforced in the tool layer, not by the model. You can audit every guardrail in code.
Where does the agent's state live?
In Postgres or Temporal, in your tenant, under your IAM. State is durable, resumable, and inspectable — we do not run agents on in-memory loops that lose context on restart.
Can we modify or override an agent decision mid-flight?
Yes. The approval console exposes pause, edit, and override on any step, and overrides are captured as labeled examples we use to tune behavior over time.
How do you measure agent quality before scaling traffic?
We run the agent against a held-out task set with labeled expected outcomes — success rate, average steps to completion, approval rate, and cost per task. Cutover thresholds are written down before traffic moves.
What happens when a model upgrade changes behavior?
Every model upgrade runs against the same task set in CI; behavior drift is loud. Rollback is a single config flag and the production model version is stamped on every audit trace.

Ready to scope this?

Thirty minutes with a principal. We will walk through your constraints and what a 30- to 90-day pilot would actually look like.