Agent Governance & Audit
Keeps an inventory, evidence trail and control set for every agent in production.
The problem. Agents accumulate faster than governance does. When the regulator, auditor or board asks what AI is running, who approved it and what it is allowed to do, the answer is a spreadsheet somebody maintains by hand.
The agent maintains a live inventory of every agent, its owner, purpose, data, permissions, approvals and risk tier.
It collects the evidence continuously — evaluation results, human-review rates, incidents, drift and access changes — and maps it to NIST AI RMF and EU AI Act expectations.
Control failures raise findings with owners and due dates rather than surfacing at audit time.
A manual register that is out of date the week it is written, and audit evidence assembled in a panic.
A live register with continuous evidence, so an audit request is answered from the system rather than from memory.
Where this agent sits in the stack.
A modular stack that separates experience, orchestration, models and data — so use cases ship independently without a rebuild each time.
- 01
Experience & copilots
Web, mobile, CRM, service desk and Microsoft 365 surfaces where employees and customers meet AI — embedded in the tools they already use.
- 02
Orchestration & tooling
Prompt and agent orchestration, tool calling, routing between models, retries, fallbacks and cost/latency budgets enforced per workload.
- 03
Model layer
Azure OpenAI, AWS Bedrock, Gemini Enterprise, Anthropic and open-weight models behind a common gateway with routing, caching and spend controls.
- 04
Knowledge & retrieval
Chunking, embeddings, vector and hybrid search, permission-aware retrieval and citation so every answer is traceable to an approved source.
- 05
Data & integration
Event streams, APIs, lakehouse and CDC pipelines connecting the stack to ERP, CRM, HCM and the core systems of record.
Integration surface
- Agent registry and deployment pipeline
- GRC and risk platform
- Observability, evaluation and incident tooling
- Identity provider and access governance
Guardrails & human oversight
- Risk tiering determines the control set; higher tiers require named human oversight.
- No agent reaches production without an owner, purpose and approval recorded.
- Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached.
- Findings have owners and due dates, tracked to closure.
What has to be true first
- An agreed AI risk-tiering standard.
- Access to deployment, evaluation and incident data.
- A GRC owner who will act on findings.
Security, data & compliance
- Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account.
- Customer and employee data stays inside your tenancy and region; no training on your data by default.
- PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy.
- Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.
How this agent reaches production.
Weeks 1–3 · Scope
Risk tiering, control mapping to NIST AI RMF and EU AI Act, evidence sources.
Weeks 4–7 · Build
Inventory, evidence collection, control checks, findings workflow.
Weeks 8–12 · Production pilot
All current agents registered with a first evidence pack produced.
Quarter 2+ · Scale & run
Continuous assurance with quarterly board and regulator-ready reporting.
What we agree to be measured on.
Ranges drawn from comparable production engagements. Your baseline is agreed before build starts, and the same numbers are reported after go-live.
| Metric | Expected range |
|---|---|
| Production agents in the live register | 100% |
| Time to produce an audit evidence pack | Weeks to hours |
| Controls evidenced continuously | Most controls automated |
| Open findings past due date | Tracked to zero |
Model the business case: Agentic AI ROI calculator →
Every current agent registered, risk-tiered and evidenced, with a first audit pack produced.
Fixed-price scope · milestone billing · price on request.
Continuous assurance across the AI estate with quarterly board and regulatory reporting.
Retained pod · quarterly outcome review · price on request.
The full Agent Governance & Audit specification, as a PDF.
A multi-page specification your architecture, security and procurement reviewers can read without a call: what the agent does, the architecture, the integration surface, autonomy and guardrails, security posture, rollout plan, measurement plan and engagement shape.
- Process before and after, with the decision that stays with a human
- Layered architecture diagram and named integration surface
- Guardrails, approval gates, escalation and audit trail
- Security, data handling and compliance posture
- Phase-by-phase rollout and the measurement plan
Building your first production-grade agentic workflow
A field-tested blueprint for shipping your first agentic AI workflow into production — the same one we use with Fortune 500 clients. Covers intent boundaries, tool design, memory, guardrails, human-in-the-loop patterns and evaluation harnesses so your first agent survives real users, real data and real audits.
Read the playbook →- Agent governance program with evaluation gates and audit trail
100% of deployed agents pass a standard evaluation gate · 30–50% shorter path from pilot to production
- Privacy and works council governance for employee agents
100% of journeys pass an evaluated release gate · 30–50% shorter approval cycle · auditable access and retention controls
- Audit-grade agent observability and tracing
100% of cases reconstructable step by step · drift detected before member impact · 30% shorter compliance approval cycle
What enterprise buyers ask about this agent.
Short, quotable answers on scope, systems, autonomy, governance, timeline, outcomes and commercial shape.
What does the Agent Governance & Audit actually do?
Keeps an inventory, evidence trail and control set for every agent in production. The agent maintains a live inventory of every agent, its owner, purpose, data, permissions, approvals and risk tier. It collects the evidence continuously — evaluation results, human-review rates, incidents, drift and access changes — and maps it to NIST AI RMF and EU AI Act expectations. Control failures raise findings with owners and due dates rather than surfacing at audit time.
Which systems does the Agent Governance & Audit act in?
It works across Agent registry, GRC platform, Observability, Identity provider. The named integration surface is Agent registry and deployment pipeline; GRC and risk platform; Observability, evaluation and incident tooling; Identity provider and access governance.
Which platforms is the Agent Governance & Audit built on?
We deliver it on the platforms you already own — Azure OpenAI, AWS Bedrock, Microsoft Dynamics 365. Pronix.ai is a systems integrator, not a software vendor, so the code, prompts and evaluation sets belong to you.
How much autonomy does it have?
Supervised autonomy. Risk tiering determines the control set; higher tiers require named human oversight. No agent reaches production without an owner, purpose and approval recorded. Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached. Findings have owners and due dates, tracked to closure.
What has to be true before we start?
An agreed AI risk-tiering standard. Access to deployment, evaluation and incident data. A GRC owner who will act on findings.
How is it secured and governed?
Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account. Customer and employee data stays inside your tenancy and region; no training on your data by default. PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy. Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.
How long does it take to reach production?
Delivery runs in 4 phases. Weeks 1–3 · Scope: Risk tiering, control mapping to NIST AI RMF and EU AI Act, evidence sources. Quarter 2+ · Scale & run: Continuous assurance with quarterly board and regulator-ready reporting.
What results should we expect?
100% agent inventory with audit evidence in hours, not weeks. Expected ranges from comparable production engagements: Production agents in the live register 100%; Time to produce an audit evidence pack Weeks to hours; Controls evidenced continuously Most controls automated; Open findings past due date Tracked to zero. Your baseline is agreed before build starts and reported on the same measures after go-live.
What does an engagement look like?
Production pilot: Every current agent registered, risk-tiered and evidenced, with a first audit pack produced. Scale and run: Continuous assurance across the AI estate with quarterly board and regulatory reporting. Fixed-price pilot with milestone billing, then a retained pod with quarterly outcome reviews.
What does the Agent Governance & Audit cost?
Price on request. Every agent is scoped to your integration surface, data readiness and compliance posture, so we send a written estimate after a 30-minute scoping call rather than publish a band that would not apply to you.
Scope the Agent Governance & Audit against your systems.
A 30-minute working session with the architects who deliver it: your integration surface, the decision that stays with a human, the measurement plan and a written estimate. We reply within one business day.
Get a written estimate for the Agent Governance & Audit.
Tell us the process, the systems it touches and the compliance scope. We come back with a scope, a measurement plan and a written estimate — no published band that would not apply to you.
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