Platform · AI Agent Governance

Govern AI agents from design to runtime

Q: Does building or managing an agent require different permissions than everything else in Celigo?

No. Agents use the same account roles you already have, like Manage and Monitor. There’s nothing agent-specific to configure.

Q: How do we audit what an AI agent did, and prove it to a compliance team?

Every agent action and guardrail check is captured in Celigo’s existing logs, metrics, and audit trails, the same system that already covers flows and integrations. That trail is what you’d pull for a SOC 2 review or an incident reconstruction.

Q: Is there a difference between guardrails for agents and guardrails elsewhere in Celigo?

No. A guardrail is a flow step that can run before an agent, after an agent, or anywhere else in a traditional flow or API. The mechanics and the fact that it flags rather than enforces are the same everywhere it’s used.

Guardrails, execution logs, audit trails, and access controls that help teams govern AI agents from design through production.

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AI agent conversation showing two guardrail policy checks passing before the agent continues.

What AI TRiSM means for Celigo AI agents

AI Trust, Risk, and Security Management, or AI TRiSM, is the discipline of governing AI systems across trust, risk, and security. For enterprise teams, that governance cannot stop when an agent is built. It needs to apply for as long as the agent runs inside business workflows.

At Celigo, that discipline shows up through connected platform capabilities: guardrails that evaluate what an agent receives and produces, execution logs that help teams understand what happened during an agent request, audit logs that track relevant user and configuration activity, and access controls that govern who can build, manage, or monitor AI-enabled automation.

These controls are part of the same platform teams already use to build and operate integrations.

Celigo AI agent run detail showing governance information for an enterprise workflow.

Governance built into the platform, not bolted on

There is a real difference between governance as part of the platform and governance as a separate product added on top of it.

Because agents, guardrails, and flows operate within the Celigo platform, teams can apply governance using familiar controls: permissions, execution logs, audit logs, error management, monitoring, and flow logic.

That means teams do not need to manage a separate governance system just because a workflow includes an AI agent.

Celigo platform controls for governing AI agents alongside integrations.

Guardrails

AI agent guardrails: Policy checks before and after agent actions

AI agents do not exercise judgment the way people do. They act based on whatever instructions, tools, and context are available to them, including content from documents, emails, web pages, and connected systems, and that content can be shaped by something malicious the agent was asked to read.

Celigo guardrails let teams check that context before and after agent actions. They evaluate inputs and outputs, then return structured signals your workflow can use to stop, route, retry, skip, or review the next step.

That keeps policy outside the agent’s reasoning and inside the governed workflow around it.

How Celigo guardrails flag policy violations

Celigo Guardrails evaluate each record against a safety, compliance, or governance policy before or after an AI agent runs.

Guardrails can be configured for content moderation, PII detection and masking (or redacting), or custom AI policy checks. Each guardrail returns schema-validated JSON with a deterministic result: flagged true when the record violates the configured policy, flagged false when it does not.

Guardrails do not enforce actions on their own. Their structured output is used by Celigo flow logic to route, stop, retry, skip, or send records for review.

Celigo flow routing a record after an AI agent guardrail flags a policy violation.

Wiring guardrail verdicts to stop, route, or redact actions

Guardrails flag. They do not enforce actions by themselves.

What happens after a guardrail returns its result is configured by your team using standard Celigo flow logic. A flow can stop a record, route it to a person, skip it, send it down an exception path, or continue processing based on the guardrail output.

This keeps the enforcement logic visible within the flow rather than hiding it in a black box.

Celigo flow actions configured from guardrail verdicts to stop, route, or redact records.

Guardrail trace view for every policy check

Guardrail traces provide session-level visibility into how a policy check evaluated a record. A trace can show:

The type of guardrail policy
The configuration or thresholds used
The outcome
The categories or entities that triggered the result
High-level timing and usage information

This helps teams debug why a record was flagged during a preview run or runtime session.

Guardrail trace details showing how a policy check evaluated a record.

Chaining guardrails across an agent workflow

A guardrail can run before an agent to evaluate input before it reaches a model — checking for unsafe content, prompt-injection signals, or PII. A guardrail can also run after an agent to evaluate generated output before it reaches a downstream system.

Guardrails can be chained, so a record can pass through multiple policy checks in sequence, such as PII detection, content moderation, and a custom policy check. Each structured result can feed the next decision in the flow.

Celigo workflow chaining multiple guardrail checks around an AI agent.

Agent Trace

See why an agent took an action with Agent Trace

An agent’s output is only as trustworthy as your ability to see how it got there. Celigo captures both the what and the why natively within Execution Logs, right alongside exports, imports, and guardrails:

Request: The actual input given to the agent, including general details like the model used and the instructions or prompt.
Response: The agent’s response to that input, including total tokens consumed and their cost, plus detailed LLM Reasoning showing why the agent did what it did.
Raw trace: A complete end-to-end execution record, including full reasoning — a superset of everything in Request and Response — for anyone who wants the full, unfiltered trace.
Tools execution: If your agent has tools configured, you’ll also see which tools were executed, their status, and the exact input and output for each.

Execution logs

Monitor AI agent activity with execution logs

An agent’s output is only as useful as your ability to understand what happened during the request.

Celigo execution logs help teams monitor and troubleshoot AI agent and guardrail activity using the same logging tools they already use for flows.

Basic logging is designed for production support and can show key details such as:

Agent invocation
Timestamp and step duration
Model used
Token and credit usage
Tools usage
Errors and recovery details
Final outcome

This helps teams monitor production activity, track usage, investigate failures, and confirm that requests completed successfully.

Detailed logging

Use detailed logging for deeper troubleshooting

When deeper troubleshooting is needed, detailed logging can provide additional diagnostic data, such as:

Agent reasoning summaries
Tool execution traces
Model output
Raw LLM request and response details

Detailed logging is optional and should be enabled only when needed for controlled troubleshooting or behavioral analysis. Because detailed logs may include sensitive content, they should be used under strict governance controls.

Tools

Track tools used by an agent

When an agent has tools configured, execution logs can help teams understand which tools were called, whether they succeeded or failed, retry attempts, and input/output summaries.

This gives teams visibility into how an agent interacted with the tools available to it, without treating agent activity as a separate black box outside normal flow operations.

Audit trails

Using audit trails for compliance and incident review

That audit trail is what turns “we think the agent behaved correctly” into something you can actually demonstrate. It’s the record you pull during a SOC 2 review, the trail you reconstruct after an incident to understand exactly what happened and in what order, and the evidence you point to when someone asks whether an agent stayed within its configured policy.

Error handling

Standard error handling for AI agent exceptions

When an AI agent step encounters an error, it follows Celigo’s standard import-step error handling and run behavior. Errors are captured through Celigo’s existing execution logs and error-management tools, where teams can review details, investigate with trace keys, retry, resolve, and assign issues using the same operational process they already use for other flow errors.

Access control

Role-based access control and identity for AI agents

Icon representing Manage and Monitor role controls for AI agents.

RBAC for Agent Builder and agent actions

Access to AI agents is governed through Celigo’s existing account and integration permission model.

Teams can use familiar role-based access controls, including roles such as Manage and Monitor, to determine who can build, manage, monitor, or review AI-enabled automation.

This lets teams apply the same access approach they already use for flows and integrations.

Icon representing SSO and OpenID Connect authentication for AI agent access.

SSO and centralized authentication with OpenID Connect

Celigo supports OpenID Connect-based SSO, allowing teams to centralize authentication for users who build, manage, or monitor platform resources, including flows that use AI agents.

This helps organizations align AI-enabled automation with their broader identity and access management practices.

Icon representing one permission model across flows, APIs, and AI agents.

One permission model across flows, APIs, and agents

The RBAC and SSO that already govern a flow govern an agent too. There’s no second permission model to learn, configure, or keep in sync just because a workflow now includes an AI step.

Contain an agent by controlling what it can reach

Celigo gives teams multiple ways to control what an AI agent can reach.

A team can disable the flow an agent runs in so the agent is no longer invoked by that flow. A team can remove a tool, which directly revokes the agent’s ability to execute specific actions. Teams can also control access to the MCP server, or down to the individual tools exposed through it.

Administrators can inspect an agent’s configuration and available tools to understand its operational boundaries — what the agent can do — without requiring a separate security model.

Agent tool access controls for ERP order lookup, Gmail email, and ERP refund actions.

Limits on repetitive or excessive tool calls

AI agents need boundaries when they interact with tools.

Celigo includes limits designed to reduce the risk of repetitive or excessive tool calls, helping prevent runaway agent behavior.

This is narrower than full behavioral anomaly detection, but it provides a practical control against repeated tool calls that exceed expected behavior.

Celigo notification showing a repeated AI agent tool-call limit.

Lifecycle

Policy enforcement across deterministic and agentic workflows

Governance in Celigo doesn’t stop at the edge of an agent.

Because Celigo AI agents are configured as import steps within flows, agent-related changes can participate in the same flow lifecycle practices teams use for integration changes, including non-production testing, promotion, snapshots, and rollback through Integration Lifecycle Management where supported.

Teams can build and test changes in a non-production environment, review what changed before promotion, and revert to an earlier configuration if something in production does not behave as expected.

That matters because an agent is not a special case outside normal change management. It is part of the automation spectrum, alongside rules-based flows, integrations, guardrails, and workflows that route to people for review.

AI agent governance for IT and security leaders

Scoped access, a full audit trail, and direct control over what an agent can reach, without adopting a separate security model just because a workflow involves an AI agent instead of a traditional integration.

AI agent governance for compliance and risk teams

Structured, audit-ready evidence of what an agent did and why, tied to policy checks that already have a name, content moderation, PII detection, or a custom policy you defined yourself, rather than a plain-language summary you have to take on faith.

AI agent governance for builders and business teams

Build an agent with guardrails, tool-level access control, and an audit trail already wired into the platform you're building it in, instead of hand-building a separate approval or logging system around it after the fact.

What is AI agent governance, and how does Celigo implement it?

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AI agent governance is the set of controls that determine what an AI agent can do, who’s accountable for it, and what evidence exists once it acts. This is the integration capability Gartner evaluates as support for the development and orchestration of AI agents. Celigo implements it through guardrails that flag policy violations, an agent trace and audit trail for every action, role-based access control using the same roles you already have, and direct control over the flows, tools, and MCP servers an agent depends on.

Do Celigo guardrails automatically stop a non-compliant agent action?

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No. A guardrail flags a record as violating or not violating a configured policy and returns that result with supporting detail. What happens next, stopping the record, routing it to a person, redacting a field, is configured by you using standard flow branching. This keeps the enforcement logic visible in your flow rather than hidden inside the guardrail itself.

Can we contain or stop an AI agent in production?

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Yes, several ways. Disable the flow an agent runs in, and it stops immediately. Remove or disable a specific tool to take away just that capability. Disable an MCP server or the individual tools it exposes to cut off access at that layer. Celigo also automatically limits repetitive or excessive tool calls from a single agent, so a runaway loop doesn’t run unchecked.

Does building or managing an agent require different permissions than everything else in Celigo?

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How do we audit what an AI agent did, and prove it to a compliance team?

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Is there a difference between guardrails for agents and guardrails elsewhere in Celigo?

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Govern every agent the same way you already govern everything else

Guardrails, execution logs, audit trails, and access control that live inside the platform where your agents are built and run, not a separate system to configure and maintain.