Agentic AI

Somebody Has to Answer for What the Agent Did

Not the vendor. Not the model provider. You. Every AI system in your organization eventually produces an outcome someone has to defend, and the quality of that defense is decided long before the incident, by whether anyone wrote down what the system was allowed to do and how you would know if it stopped doing it.
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See the framework
The premise

Governing Tools Is the Wrong Unit of Analysis

Most AI policies are written against a list of approved tools. That approach fails immediately, because the same tool is trivial in one context and consequential in another, and because the list is out of date the week it is published.

Tiered by use

The only unit that holds

A summarization model drafting internal notes and the same model screening applicants carry entirely different obligations. We tier by what the system decides and who it affects, which stays stable as the tool landscape churns.

Shadow AI is in scope

Because it exists

Unsanctioned tools and business-bought subscriptions are governed as first-class citizens rather than treated as a policy violation to be discovered later. A framework that pretends they are absent is governing a fiction.

Written to be used

Not to be filed

The framework ships with a practitioner field guide, because a policy nobody can operationalize produces documentation rather than governance.

One partner, strategy through steady state

Advise

Advisory

Build

Implementation

Migrate

Migration

Run

Managed Services

Extend

Custom Development

Optimize

FinOps

The framework

Five Components

Sector neutral at the core, with extensions where a regulator demands more. Each component is designed to be implemented independently and to work harder when combined.
01

Policy and oversight

Decision rights, review board structure, and approval workflow. What an AI system may do without a human in the path, who decides that, and what happens when the answer changes.

02

Inventory and tiering

A live register of AI in the organization, tiered by use rather than by tool, covering systems you built, systems you bought, and systems that arrived without anyone approving them.

03

Evaluation and assurance

Evaluation criteria per tier, regression testing, and acceptance thresholds, so a model change or a data drift does not silently degrade a production decision.

04

Monitoring and response

Continuous monitoring, drift detection, incident classification, and rollback. Every AI system eventually produces a wrong answer, and the framework decides whether that is an event or a crisis.

03

Sector extensions

Overlays for regulated environments where general governance is insufficient, developed with the client against the specific evidence their regulator requires.

06

The practitioner companion

The working document that turns the framework into daily practice, covering how to tier a system, run a review, document a decision, and escalate an incident.

Scope

Built, Bought, and Everything Else

Three columns, one framework. The obligations differ, the tiering logic does not.

Built

Systems you engineered
  • Full lifecycle documentation and design records
  • Evaluation harnesses owned in house
  • Training and retrieval data provenance
  • Access model and boundary documentation
  • Change control across model and prompt versions
  • Incident ownership sits with you

Bought

Vendor and embedded AI
  • Vendor assessment and due diligence criteria
  • Contractual evidence and audit rights
  • AI features arriving inside existing software
  • Data residency and processing commitments
  • Dependency on vendor evaluation you cannot inspect
  • Escalation routes when the vendor is the failure point

Shadow

AI nobody approved
  • Discovery of unsanctioned tools in active use
  • Business-bought subscriptions outside procurement
  • Risk triage rather than blanket prohibition
  • Sanctioned alternatives where the need is legitimate
  • Data exposure assessment for what already left
  • A path to bring usage into the register

Where this connects

Governance Rests on Lineage

An AI governance framework with no data lineage beneath it produces policy statements nobody can evidence. When a regulator asks how a decision was reached, the answer runs through the retrieval source, through the pipeline, and back into the system of record.

This is why we implement AI governance alongside data governance rather than as a separate program. Built once, the same layer serves the model risk documentation and the audit request.

Connected services

  • Data Governance & Lineage: the evidence layer underneath
  • Agentic AI Engineering: guardrails and escalation in the build
  • AI-Enabled Managed Services: monitoring in steady state
  • AI Readiness Assessment: where governance gaps surface first
  • Security & Resilience: the wider control environment

One layer, two obligations

Accelerators

Patterns We Have Already Built

Agent programs do not start from a blank page here. AgentIQ carries the orchestration, evaluation, and governance patterns from prior delivery into the next engagement.
Accelerator

Document Intelligence

Extraction, classification, and validation across unstructured enterprise documents, with confidence thresholds and human review routing.

Accelerator

Invoice Automation

Straight-through invoice processing with exception routing and a complete audit trail for finance and audit.

Accelerator

Service Desk Agent

First-line support resolution and triage with defined escalation to human agents, across voice and text channels.

Accelerator

Configure, Price, Quote

Quote generation and configuration logic driven by an agent working over product, pricing, and eligibility data.

Start here

Find out what your organization would have to defend today

The governance assessment inventories the AI in active use across built, bought, and unsanctioned categories, tiers it by use, and identifies where your current evidence would not survive a serious question. It produces a framework implementation plan sequenced by exposure.
Request a governance assessment

What you walk away with

  • AI inventory across built, bought, and shadow categories
  • Tiering of every system by use and impact
  • Gap analysis against your regulatory evidence requirements
  • Policy and decision rights recommendations
  • Evaluation and monitoring requirements per tier
  • Sequenced implementation plan by exposure and effort

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