Guardrails first.
Then scale the agents.
We help teams design the governance model behind AI adoption. Traceability, approvals, security, oversight, and policy controls so your agents are useful on Monday and defensible on Friday.
Built on recognized frameworks:
What this page is really about
Most teams do not fail because the model is weak. They fail because nobody decided what the system is allowed to do, what it must show its work on, who approves edge cases, and how the business will know when it goes sideways.
Guardrails
Prompt boundaries, action constraints, approved tools, escalation logic, and human checkpoints so agents stay inside the job you actually assigned them.
Traceability
Source visibility, decision logs, handoff history, model and prompt versioning, and evidence trails so teams can answer what happened and why.
Security
Identity, least privilege, connector review, data boundaries, environment separation, and admin controls that keep AI from becoming a side door into your stack.
Oversight
Evaluation, exception handling, incident response, and operating reviews so the system stays trustworthy after launch instead of drifting into chaos.
What we cover
The operating system behind trustworthy AI
This is where governance stops being vague PowerPoint language and turns into concrete controls your operators, admins, and leaders can live with.
Human approval design
Approval gates before send, publish, or purchase
Tiered review paths by workflow risk
Escalation rules for low-confidence outputs
Identity and access
Least-privilege role design
Tool and connector scoping
Environment and tenant separation
Traceability and auditability
Action logs and source receipts
Prompt, model, and workflow version control
Clear ownership for overrides and exceptions
Data boundaries
Approved source allowlists
Sensitive data handling rules
Internal versus external usage policies
Risk and policy model
Use case tiering by business impact
Control mapping by workflow type
Operating rules the team can actually follow
Monitoring and response
Regression and evaluation routines
Failure mode playbooks
Rollback and incident-response paths
Microsoft Agent Governance Toolkit
The numbers behind application-layer enforcement.
Microsoft's open-source Agent Governance Toolkit found that prompt-only safety controls allowed a 26.67% policy violation rate. Application-layer enforcement - deterministic policy evaluation before each tool execution - dropped that to 0.00%.
That gap is why governance cannot live only in the system prompt. It has to be wired into the architecture - at the decision points before the agent acts, not after.
26.67%
Policy violation rate with prompt-only safety controls
0.00%
Policy violation rate with application-layer enforcement
The attack surface most teams ignore
Agentic AI introduces risks traditional security does not cover.
When AI agents can read documents, call APIs, write to systems, and hand off to other agents, the threat model changes. These are the three categories that most governance frameworks miss.
Prompt injection
Malicious instructions embedded in documents, emails, or tool outputs that hijack agent behavior mid-task. An agent processing external content can be redirected without the user knowing. Guardrails need to cover input sources, not just user prompts.
MCP tool poisoning
Model Context Protocol tools that look legitimate but carry embedded instructions designed to manipulate agent behavior or exfiltrate data. As MCP adoption grows, unreviewed tool connections become a meaningful attack vector that most teams have no inventory process for.
Shadow AI discovery
Employees using unauthorized AI tools, connecting unapproved APIs, or building ad-hoc agents outside any governance framework. Shadow AI bypasses every control you have designed. Discovery and inventory are a prerequisite for governance, not a nice-to-have.
Where Agent 365 fits
Agent 365 covers the Microsoft control surface.
If the team lives in Microsoft 365, governance cannot stay abstract. It has to show up where agents are actually deployed, where permissions actually exist, and where users actually interact with them. That is the job Agent 365 helps cover.
Microsoft-native agent deployment inside Copilot, Teams, Outlook, SharePoint, and related M365 surfaces
Graph and connector scoping so agents only see the data and actions they are supposed to see
Tenant-aware guardrails, approval paths, and role-based access patterns for internal and customer-facing use cases
Operational controls for skills, declarative agents, connectors, and custom engine agents that live in the Microsoft ecosystem
A clean bridge between AI policy and the place users actually interact with the agent every day
Where Polygraf can fit
Polygraf is worth considering when oversight needs to get sharper.
Polygraf is an independent AI audit and monitoring platform - it gives teams a separate oversight layer that operates independently of their model and agent stack.
For some teams, native platform controls and workflow design are enough. For others, especially regulated or audit-sensitive teams, there is value in an additional policy and evidence layer around how AI work is reviewed, explained, and defended.
An optional oversight layer for teams that want stronger policy enforcement, provenance, or review depth around higher-risk workflows
Useful when the question is not just can the agent do it, but can we prove it behaved within policy
Especially relevant for regulated, client-facing, or audit-sensitive environments where evidence and defensibility matter as much as speed
Which one do you need?
If your team works inside Microsoft 365, start with Agent 365 - it is where your agents live and where governance has to show up in practice. If you need an independent oversight layer that can evaluate AI behavior across any stack, Polygraf is the right addition.
Most teams in regulated or audit-sensitive environments end up with both. Agent 365 covers the Microsoft control surface; Polygraf provides the external evidence and review layer that internal tooling cannot provide about itself.
What teams are usually missing
The gaps to close before this gets expensive
If you want the short list of what else should be on this page, it is these six things. This is the bit most teams hand-wave until the first ugly surprise.
Ownership
Who owns AI policy, exceptions, and final sign-off. If that is fuzzy, the controls will be fuzzy too.
Risk tiering
Not every workflow deserves the same friction. Teams need clear low, medium, and high-risk categories with matching controls.
Change control
Prompt edits, model swaps, connector additions, and workflow changes should not happen like someone updating a Spotify playlist.
Evaluation
Most teams test on launch day and then hope for the best. You want recurring evals, regression checks, and known failure cases.
Incident response
What happens when an agent over-shares, misroutes, or acts out of policy. Silence is not a response plan.
Adoption and training
Users need to know what the agent can do, what it cannot do, and when to override it. Governance is operational, not just legal.
We can help define the rules and build the system.
Start with the governance model. Then wire it into Agent 365, Google Agent Garden, your custom stack, or the workflows already live in your business.