Governance area
Agent identity
Define which agent is acting, which tenant or business context it belongs to and who owns its production behaviour.
AI agents become materially different from passive assistants when they can call tools, retrieve protected data, update systems, trigger workflows and coordinate actions across the business. Governance defines the boundary between flexible reasoning and controlled authority so agents can operate without becoming unbounded software actors.
Peak Demand treats agent governance as a production architecture problem. The model can interpret intent and make flexible decisions while identity, permissions, business rules, validation and high-consequence actions remain controlled.
A chatbot that drafts text creates one type of risk. An agent that can book appointments, update customer records, send communications, trigger operational workflows or call internal systems creates another. The system needs explicit authority boundaries before the agent begins acting on behalf of the business.
Define which agent is acting, which tenant or business context it belongs to and who owns its production behaviour.
Restrict APIs, functions and downstream systems to the minimum set required for the approved workflow.
Control which records, repositories and knowledge sources the agent may retrieve or expose.
Define whether the agent can read, recommend, draft, update, transact or only prepare actions for approval.
Specify when ambiguity, policy, confidence or consequence requires a person to take over.
Monitor failures, retries, tool use, model changes, incidents, cost and downstream business outcomes.
Each layer separates a different type of authority so the agent can reason flexibly without receiving unrestricted access to the business.
Assign each agent a defined service identity, tenant context, owner and authentication path.
Scope the systems, data and functions available to the agent based on its approved role.
Enforce schemas, business rules, eligibility, transaction boundaries and source-of-truth checks outside the model.
Define which actions may run automatically, which require approval and which are prohibited entirely.
Record tool calls, validation outcomes, retries, escalations, model versions and final workflow results.
Monitor reliability, incidents, cost, change, drift and business outcomes after production launch.
| Authority level | What the agent can do | Governance posture |
|---|---|---|
| Level 1 · Read | Retrieve approved information without changing enterprise records. | Scoped access, logging and source-of-truth controls. |
| Level 2 · Recommend | Analyze context and propose a decision or next step. | Human retains authority for the final action. |
| Level 3 · Draft | Prepare communications, records or transactions for review. | Approval required before submission or execution. |
| Level 4 · Act with validation | Execute approved actions when deterministic checks pass. | Strong permissions, validation, auditability and rollback. |
| Level 5 · Act with approval | Prepare high-consequence actions but wait for explicit human authorization. | Enforced approval gate with attributable decision evidence. |
| Level 6 · Autonomous action | Complete approved end-to-end workflows without routine human review. | Reserved for mature, observable, lower-risk or highly controlled processes. |
Expose only the functions required for the approved workflow rather than every available integration.
Use narrowly scoped service credentials instead of broad administrator-level access.
Check required fields, identifiers, formats and business-rule constraints before the action executes.
Do not give write authority simply because the agent needs to retrieve information from the same system.
Restrict amount, frequency, volume, resource type or other consequence-bearing dimensions where appropriate.
Record what tool was called, what validation passed and whether the downstream system succeeded.
Confirm the person, account or service before exposing protected data or actions.
Validate critical facts against authoritative systems instead of relying on conversational memory.
Enforce eligibility, routing, scheduling, financial or operational constraints deterministically.
Reject malformed, incomplete or unauthorized tool calls before they reach the target system.
Require a valid human decision before releasing restricted actions.
Handle retries, rollbacks, timeouts and escalation when a downstream dependency fails.
Multi-agent systems can improve specialization, but they also create more paths through which data and actions can move. Governance should make delegation explicit rather than assuming agents can safely hand work to one another.
Each agent should have a defined responsibility rather than overlapping authority across the same workflow.
Define which tasks may be handed to another agent and what context can travel with the handoff.
Do not assume a downstream agent should inherit the permissions of the upstream agent.
Control what shared memory, workflow state or customer context multiple agents are allowed to access.
Define which source, agent or deterministic rule wins when two agents produce inconsistent decisions.
Preserve a workflow identifier across agent handoffs so the final outcome can be reconstructed.
| Escalation trigger | Why the agent should stop | Required handoff |
|---|---|---|
| Low confidence | The agent cannot establish enough certainty to act safely. | Intent, known facts and reason for uncertainty. |
| Policy exception | The request falls outside the approved workflow or authority level. | Request summary and applicable policy boundary. |
| Conflicting system state | Source-of-truth records disagree or a required condition cannot be verified. | Relevant records, conflict and attempted validation. |
| Tool failure | A required system is unavailable, inconsistent or partially updated. | Tool-call history and current workflow state. |
| High consequence | The action requires explicit human authority. | Proposed action and verified supporting context. |
| User request | The customer or employee asks for a human or needs a service path AI should not own. | Conversation context without forcing repetition. |
Track whether actions reached the right system with valid parameters and completed successfully.
Measure how often deterministic controls block proposed actions and why.
Understand which scenarios the agent cannot resolve autonomously.
Detect repeated tool calls, stalled workflows and inefficient reasoning loops.
Measure model and tool cost relative to completed business work.
Track whether the agent actually finished the intended workflow rather than merely producing a response.
Document the agent role, users, systems, data, tools, authority and expected business outcome.
Assess consequence, autonomy, data sensitivity and reversibility to determine required controls.
Implement identity, permissions, validation, observability, escalation and safe failure handling.
Test normal cases, edge cases, prohibited actions, tool failures, conflicts and human handoffs.
Monitor reliability, tool use, incidents, model changes, cost and business outcomes.
Disable credentials, remove tool access, archive required evidence and decommission integrations cleanly.
New model behaviour can affect tool choice, reasoning, latency and escalation.
System instructions can materially alter priorities, tone, decision paths and boundary handling.
New functions, schemas or permissions can expand or alter what the agent can do.
Eligibility, routing, scheduling or transactional rules can change downstream outcomes.
New retrieval sources can change what information the agent sees and trusts.
New handoffs or action sequences can introduce different failure and governance paths.
Remove access to a failing API while preserving unaffected agent capabilities.
Preserve information access while temporarily removing system-write authority.
Route all relevant cases to a person while an issue is investigated.
Lower transaction scope, frequency or volume during elevated risk.
Restore a known-good model, prompt, rule or workflow version after regression.
Use a defined kill path when continued operation would create unacceptable business risk.
The agent relies heavily on instructions with broad tool or data access and limited deterministic control.
Tool access is narrowed and the agent has a more explicit role and workflow boundary.
Identity, permissions, validation and business rules move outside the model.
Tool calls, escalations, failures and business outcomes are measured continuously.
Autonomy expands or contracts based on production evidence and workflow consequence.
Multiple agents share enterprise standards for identity, permissions, auditability and operating ownership.
How often the agent completes the workflow correctly, including appropriate escalation where required.
How often the correct tool is called with valid parameters and completes successfully.
How frequently deterministic controls block agent-proposed actions before execution.
Whether the right cases reach people with enough context to continue the work.
Attempts to access tools, data or actions outside the agent's permitted scope.
Total model and tool cost relative to the business work successfully completed.
Map role, tools, data, authority and escalation before production implementation.
Keep identity, permissions, validation and high-consequence business logic outside the model.
Scope credentials, validate payloads and preserve source-of-truth discipline across enterprise systems.
Evaluate unauthorized requests, missing data, conflicts, tool failure, loops and human escalation.
Capture tool calls, control outcomes, escalations, model versions and final business results.
Expand autonomy only when production reliability, controls and business outcomes support the change.
AI agent governance is the framework used to control an agent's identity, permissions, data access, tools, autonomy, validation, escalation, auditability and production operations.
Model governance focuses on selecting, evaluating and changing the intelligence layer. Agent governance covers the full production system around the model, including tools, permissions, actions, workflow state and human escalation.
Generally no. Agents should receive the minimum permissions required for the approved workflow, with read and write authority separated where possible.
Identity, permissions, validation, high-consequence business rules, action limits, approval gates and source-of-truth checks should generally remain outside model reasoning.
Define each agent's role, permissions, delegation rules, shared-state access, conflict resolution and end-to-end traceability across handoffs.
Common triggers include low confidence, policy exceptions, conflicting records, tool failure, high-consequence actions and explicit user requests.
Operators can disable tools, switch to read-only mode, force human review, reduce transaction limits, roll back releases or stop the agent entirely.
Yes. Peak Demand can design and implement agent permissions, deterministic middleware, integrations, validation, observability, escalation and production operating controls.
Peak Demand can design the permissions, deterministic middleware, tool controls, escalation paths and production observability required for governed enterprise AI agents.