Use this page to design the parent control record. The current generic market is drifting toward broader governed-agent and agentic-operations language, so this framework now makes that connection explicit: the control record is the mechanism that turns agent policy into a governed operating boundary. Use the linked specialist guides for evidence gates, human review and CRM readiness instead of forcing this page to repeat their implementation detail.
| Dimension | Implementation record | Buyer test |
|---|---|---|
| Identity | A unique workload identity, owner, delegation chain and expiry | Who acted, for whom, and for how long? |
| Permission | Exact tool, operation, target, record class and volume boundary | What is the smallest power this action needs? |
| Decision | Deterministic allow, deny, narrow or human-review rule outside the prompt | What context changes the decision? |
| Evidence | Linked request, inputs, policy result, tool result and business outcome | Can an operator reconstruct the action? |
| Recovery | Idempotency, saved prior state, retry boundary, compensation and kill path | Can the team contain and reverse failure? |
| Autonomy gate | Dated test and production thresholds for expanding one boundary | What evidence earns more authority? |
Scope: one agent, one proposed consequential action and one target system boundary. Source basis: NIST identity-and-authority guidance, Microsoft's centralized governance baseline for AI agents, and Google Cloud governance patterns that bind agent behavior to registries, gateways and data-source controls. Limitation: this is Brainiac’s implementation method, not a standard, certification, legal control catalogue or claim of measured client outcomes. Platform-specific controls still need architecture and legal/security review.
What is a governed AI agent?
A governed AI agent is not merely an agent with policy text in its prompt. It is an agent that operates under a visible control plane: named ownership, a distinct identity, constrained tool and data access, continuous monitoring, and explicit intervention paths when behavior falls outside policy. Microsoft’s current enterprise guidance frames that baseline as enforceable governance across identity, data, security and development standards; Google’s current governance docs similarly center registries, gateways, and policy-bound data access.
That matters for buyers because many “agentic operations” claims still mix platform capability with operating control. A governed agent implementation should tell you which registry records the agent, which identity it uses, which actions and data sources are permitted, and which evidence determines whether more autonomy is allowed.
How do protocols fit into governance implementation?
Protocols are not a side issue. They are part of the boundary. Microsoft’s June 26, 2026 governance guidance explicitly recommends standardizing agent protocols such as MCP and A2A so tool use, data access, and agent-to-agent coordination remain predictable and enforceable. Google’s August 11, 2026 governance template shows the same idea in practice by routing agent data access through an MCP server tied to an agent registry and runtime identity.
In practical terms, the control record should name the protocol and integration path for each consequential action. If the team cannot say how the agent reaches a tool, how that access is constrained, and how the call is audited, the governance design is still incomplete.
How do you implement the control record?
- Map one consequential workflow. Name the business owner, exact outcome, systems, data and every read, draft, write, send, spend or delete action.
- Create the six-dimension record. Bind identity, permission, decision, evidence, recovery and autonomy gate to the action—not merely to the agent job title.
- Test controls before production. Use representative normal, edge and failure cases. Confirm deny paths, expired decisions, idempotency and rollback.
- Release the smallest authority. Begin read-only or draft-only. Permit one reversible write only when its evidence gate passes.
- Review evidence and expand one boundary. Compare accepted results, corrections, blocked actions and recovery time; then scale, refresh or stop.
Which decisions belong outside the prompt?
Put permissions and consequential action decisions in a control layer that can inspect the authenticated actor, exact tool operation, target, data class, record count, amount, evidence and recovery state. Prompt instructions can guide behaviour; they should not be the final authorization boundary. NIST’s February 5, 2026 concept paper identifies agent identification, authorization, auditing and non-repudiation as active implementation questions rather than a completed certification regime.
How should an enterprise separate experimentation from production?
Separate environments and authority by risk and purpose. Microsoft’s current governance-and-security baseline says every environment should enforce consistent controls before agents operate. Google’s current governance documentation similarly treats gateways, monitoring and security findings as part of the operating surface, not an afterthought. The practical implication is simple: sandbox freedom does not transfer directly into production authority.
Use experimentation to discover useful workflows and failure modes. Use production to enforce the smallest durable authority. If the same identity, tools, logs and approval paths span every environment without change, the separation is probably too weak.
What evidence earns more agent autonomy?
Define the autonomy gate before launch: representative tests passed, prohibited actions blocked, correction rate within tolerance, traces complete, rollback exercised and an accountable owner accepting the residual risk. Expand one boundary at a time. The pilot-to-production evidence-gate guide owns the detailed release sequence.
When is human review a real control?
Human review is meaningful only when the reviewer sees the exact action, material context, scope, evidence, expiry and recovery option and the resulting decision cannot be reused after the payload changes. See human-in-the-loop approval that actually controls an AI agent.
What must be ready in CRM before agents can write?
Governance cannot repair unclear lifecycle definitions, broad credentials, missing owners or unrecoverable bulk updates. Validate those foundations with the CRM readiness guide for AI agents, then use the AI Workflow Opportunity Mapper to choose a bounded starting workflow.
Where does this fit in Custom Agent Deployment?
This framework supports Brainiac’s Custom Agent Deployment offering. Brainiac maps the workflow and control boundaries, implements the smallest production action, and expands it only when agreed evidence supports the next authority level. It does not imply Atlas product proof, a deployment timeline, a security certification or a guaranteed performance outcome.
Sources and currency
- NIST: Identity and Authority of Software Agents, released February 5, 2026. Concept paper; active work, not certification.
- NIST AI Agent Standards Initiative, released February 17 and updated February 18, 2026.
- Microsoft Learn: Govern and secure AI agents, last updated June 26, 2026.
- Google Cloud: Govern your agents, last updated August 21, 2026 UTC.
- Google Cloud: Agent Platform with governance template, last updated August 11, 2026 UTC.
Scope one governed agent action
Use a Custom Agent Deployment working session to define one workflow, its six-control record, the protocol boundary around each action, and the evidence required before authority expands.
Discuss Custom Agent DeploymentEnd state: a scoped consultation request for the exact workflow, protocol boundary, and control record.