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AI Agents

We build agents that execute real work — reading systems, taking actions, escalating when uncertain — inside boundaries your risk function has approved. Every action is logged, attributable, and reversible.

When you need this

The situations we are called into.

A workflow needs judgment at machine speed: triage, reconciliation, case preparation, first-line response.

You want automation, but every action must have an audit trail and a human escalation path.

Earlier RPA investments break whenever an interface or a document format changes.

Approach

How the engagement runs.

Workflow decomposition

The process mapped into steps that are deterministic, judgment-based, or human-only. Agents get the middle category.

Tool and permission design

Exactly which systems the agent may read, which actions it may take, and which require sign-off.

Construction with evaluation

The agent built against a scored test suite of real cases, including the adversarial and the ambiguous.

Supervised rollout

Production traffic with human review of every consequential action until the error data says otherwise.

Autonomy by evidence

Permissions widen only where measured performance justifies it — a documented decision each time.

Deliverables

What you hold at the end.

Production agent system

Permission and escalation model

Complete action audit log

Evaluation suite with real-case coverage

Supervision console and runbook

Related research

Technical report · 2026

Evaluating large language models for regulated enterprise workflows

A practical evaluation framework for LLM systems in banking, insurance, and government contexts — metrics, test harnesses, and a taxonomy of failure modes.

In preparation

White paper · 2026

Multi-agent systems in production: reliability patterns

Failure modes observed when orchestrating multiple LLM agents on business-critical tasks, and the containment patterns that keep them recoverable.