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SERVICE /01 — AGENTIC AI

Agentic AI.

We design and deliver secure, governed AI agents that connect to the tools and workflows your teams already use. Each agent is built to know what it can act on alone, and when to put the decision back in front of a person.

Scope an engagementSee it in production →
service-agentic-ai-decision-flow
01 — THE HANDOFF PROTOCOL

Agents act inside your tools.
People keep the decisions that matter.

STEP·1 — FRAME THE DECISION
Start with one decision, written down

We define the decision the agent will own, what a good call looks like, and the actions it is never permitted to take on its own.

STEP·2 — CONNECT THE TOOLS
Ground it in the systems you run

The agent is wired into the tools and data your teams already use, with scoped permissions that mirror what a person in that seat would hold.

STEP·3 — GOVERN THE HANDOFF
Route by confidence, log everything

Actions inside the agreed bounds run unattended, anything ambiguous escalates to a named person with the reasoning attached, and every route is logged so it can be replayed in evaluation.

Multi-agent orchestration and tool-use design Scoped tool access and credential handling Human-in-the-loop escalation and approval paths Agent evaluation, tracing and regression suites
02 — RELATED WORK

Agents that can explain themselves, in workflows where that is not optional.

The closest example is BotSupply, where we built a governed AI assistant into clinical workflows for decision support and patient engagement, with explainability and human oversight designed in rather than added later. Clinical work sets the hardest bar for this: an agent that cannot show its reasoning does not get used. The same handoff protocol applies anywhere an autonomous action carries consequence.

Read the case study →
service-agentic-ai-decision-flow

Which decision would you
let an agent make first?

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FAQ

Questions about agentic AI.

What exactly is an agent, versus a chatbot or a model?

An agent plans, retrieves, reasons, and acts — using enterprise tools and memory grounded in your systems. Unlike a chatbot that answers or a model that scores, an agent carries a task through to a decision, then either executes it or hands it to a person with full context attached.

How do you keep an autonomous agent from acting on something it should not?

Confidence routes the decision. High-confidence actions ship automatically; ambiguous ones defer to a human by design, with the full reasoning trail attached. The handoff protocol is the point — agents act where they are certain and escalate where they are not.

Is this auditable enough for a regulated environment?

Yes. Every route — automated or escalated — is logged and explainable, so auditors can trace why the agent acted or deferred. Evaluation and continuous-improvement loops keep the system accurate while keeping the audit trail intact.

Where should we point an agent first?

At a high-volume, well-bounded decision where the cost of a slow call is real and the rules are clear enough to reason over — pricing moves, reorder points, alert triage. We scope the first decision with you so the agent proves its value before it earns more autonomy.