Warsha · AI agent governance
AI agent governance, built into how agents are made and run.
Governing AI agents means knowing every agent, limiting what each one can do, proving it works before release and keeping a person in charge of actions with real consequences. Warsha builds these controls into the platform where your agents are built, tested and operated, on your own servers.
- An owner and a version for every agent
- Tests and approval before release
- Allow-listed tools and human approvals
Warsha · Agent studio
Private modelsAgents
14
6 in production
Evaluation
411/420
passed
Cost today
AED 212
- Need Investigator v1.8Candidate vs v1.7 · 9 failed casesEvaluate
- update_workflow toolHigh impact · waiting for a personApproval
- Knowledge collectionConnector credential expiredSync failed
Authorized vendor to
- Ministry of Finance
- Ministry of Interior
- DGE Abu Dhabi
- Etihad Aviation
- EDGE Group
Delivered for
- Hindustan Unilever
- Nokia
- Linde
- USA Today
- JSL
Procurement-ready
- Emirati national company
- National ICV certificate
- Abu Dhabi Government Procurement Gate
- UAE Ministry of Finance, federal procurement
- Registered ADNOC vendor
What it covers
What AI agent governance means in practice
An agent doesn't only answer: it calls tools, changes records and sends messages. Governance has to cover what it does, not only what it says.
An inventory
Which agents exist, who owns each one, which version is live and what it is allowed to do.
Least privilege
Each agent reaches only the tools and data its job needs, and nothing else.
Evidence before release
Tests on known cases and attack cases before an agent goes live, compared with the version it replaces.
People in charge
An authorised person approves actions with real consequences before they happen.
A trace of every run
Every run, tool call and decision can be looked up and explained later.
Cost and accountability
Spend is visible by department, agent and model, so each owner answers for their agents.
Frameworks
What the frameworks ask, and where Warsha keeps the record
Warsha doesn't make you compliant on its own. It gives your governance team the controls and records these frameworks ask for.
| Framework | What it asks for | In Warsha |
|---|---|---|
| NIST AI RMF: Govern | Policies, clear roles and an inventory of AI systems | Every agent has an owner, a version, a role, a model policy and a tool list; roles come from your identity groups |
| NIST AI RMF: Map | The purpose, context and risks of each system | Each agent's role, output contract and allowed tools are written into its definition |
| NIST AI RMF: Measure | Tests and metrics, before and after release | Golden cases and red-team suites, compared with the version in production |
| NIST AI RMF: Manage | Go or no-go decisions, and responses to risks and incidents | Promotion requests with evidence, release and rollback, and a command centre for health and incidents |
| ISO/IEC 42001 | An AI management system with policies, objectives and controls | The records that system needs: agent definitions, test results, approvals and traces |
| OWASP Top 10 for Agentic Applications | Defences against tool misuse, privilege abuse and agentic supply-chain risks | Allow-listed tools through a gateway, least privilege per agent, and an MCP registry that blocks unregistered servers |
The controls
How Warsha governs each agent
Agent definitions
Each agent has an owner, a version, a role, a model policy, a tool list and an output contract.
Tool gateway and MCP registry
Agents reach tools only through a gateway that checks an allow-list, and every call is written to the audit trail.
Model gateway
One controlled door to every approved model, all running on your infrastructure, with no external model APIs.
Evaluation and red-team
A candidate version runs against golden cases and a red-team suite before release.
Approvals and guardrails
High-impact actions wait in an approvals inbox, and guardrails screen inputs and outputs.
Traces, operations and cost
Execution traces, a command centre for health and incidents, and cost by department, agent and model.
Getting started
Where to start
- 01
Find every agent
List the agents and pilots you run, with their owners, tools, data and models.
- 02
Set the limits
Give each agent its allowed tools, data and model policy, and decide which actions need a person.
- 03
Gate every release
New versions pass golden cases and red-team suites, and governance approves the promotion.
- 04
Watch and review
Read traces, follow incidents and cost by department, and review each agent on a schedule.
Sources
The frameworks on this page
Checked on 14 November 2026. NIST says AI RMF 1.0 is being revised, so check the current version.
Book a demo
See governance on a live agent
We build a small agent, restrict its tools, run an evaluation, raise a promotion request and approve a high-impact action, so your risk team can see the record each step leaves.
- A live demo, not slides
- With one of your use cases
- Under NDA if you need one
4.9/5 average rating · 98% client satisfaction · 150+ clients served
FAQ
AI agent governance: questions buyers ask
What is AI agent governance?
The policies and controls that keep AI agents safe and accountable: an inventory of agents with owners, limits on the tools and data each one may use, tests before release, human approval of high-impact actions, and a trace of every run.
How is it different from AI model governance?
Model governance looks at the model: its data, accuracy and bias. Agent governance also covers what an agent can do with tools, which actions need a person's approval, and who answers for each agent.
Which frameworks apply to AI agents?
Common references are the NIST AI Risk Management Framework, a voluntary framework built on four functions (Govern, Map, Measure and Manage) with a Generative AI Profile; ISO/IEC 42001, the standard for AI management systems; and the OWASP Top 10 for Agentic Applications, which lists the main security risks of agents.
Does Warsha make us compliant?
No tool does that on its own. Warsha gives you the controls and records that frameworks ask for, such as an inventory, test results, approvals and traces, so your governance team can show how each agent is managed.
Can Warsha govern agents built elsewhere?
Warsha governs the agents built and run in it. If you have agents in other tools, we start by listing them and agree which to move into Warsha.
Do our prompts or data go to an outside AI service?
No. Every approved model runs inside your data center or approved private cloud, behind the model gateway, and no external model APIs are used.
Give every AI agent an owner, a test and a trace
See Warsha govern an agent on your own infrastructure.
Book a Warsha demo