DEV Community: AXEC

A Valid Agent Plan Can Become an Invalid Action

AXEC — Tue, 27 Oct 2026 11:37:24 +0000

An agent can start with permission to draft a production change and still have no permission to deploy it.

That sounds obvious. In practice, many authorization designs treat the agent’s initial task as the security boundary. Once approved, the agent is free to discover data, call tools, and adjust its plan.

That’s where control weakens.

A runtime decision should follow the action, not just the task description.

Consider an agent asked to prepare a production configuration change.

At the start, the request may look safe:

The user is identified.
The agent is assigned a drafting task.
The target system is known.
The expected output is a proposed change.

The agent reads configuration data, checks deployment status, discovers that the change affects a wider set of services, and produces a deployment command.

The agent’s plan has changed. The final action is no longer “draft a change.” It’s “apply a change to production.”

That should be a new authorization decision.

The policy engine needs to evaluate the agent, the user, the tool, the resource, and the current context. It may allow the draft, deny the deployment, or request approval from an authorized person.

The important part is that the decision happens at the point where authority changes.

A useful buyer test is simple:

Can your system answer these four questions for every meaningful agent action?

What did the agent intend to do?
What policy allowed or blocked it?
What had changed since the previous decision?
Who, or what, approved the next level of authority?

If the answer is only stored in a final conversation transcript, you’re missing the operational evidence.

A transcript can show that an agent said, “I’m going to deploy this.” It doesn’t prove that deployment was authorized in the context that existed when the action occurred.

Runtime authorization needs a decision record attached to the execution step. The record should explain the reason for the outcome, not just store allowed: true.

That distinction matters during an incident, an audit, or a dispute over accountability. Teams need to know whether an action was allowed because the resource was in scope, because a user approved it, or because a policy failed open.

The practical design rule is this:

Treat every material change in plan, target, tool, or context as a fresh authorization boundary.

An agent may be allowed to investigate. It may be allowed to prepare. Deployment still needs its own decision.

That is how autonomy becomes bounded authority instead of inherited permission.

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Authorization Is a Runtime Decision, Not a One-Time Permission

AXEC — Tue, 27 Oct 2026 11:37:09 +0000

An AI agent shouldn’t receive permanent permission just because its initial task looked safe.

Plans Change During Execution

Agents rarely follow a fixed path.

They retrieve new information, select tools, update their plan, and decide what to do next. Each step can change the risk of the action that follows.

A request to “prepare a production change” might begin as a low-risk drafting task. It becomes materially different when the agent discovers a live system, accesses sensitive data, or proposes an action that affects production.

That change deserves a new policy decision.

Evaluate the Full Context

A useful authorization decision needs more than the requested action.

Evaluate:

The agent making the request
The user behind the agent
The tool being called
The resource being accessed
The current task and execution context
The action's potential effect

The same agent may be allowed to read a configuration file but blocked from changing it. The same user may be permitted to request a draft but not approve its deployment.

Authorization should follow the actual situation, not just the original prompt.

Treat Plan Changes as New Decisions

A changed plan is a meaningful security event.

For example:

The agent drafts a production configuration change.
The agent identifies the deployment tool.
The agent attempts to deploy the change.

The first step may be allowed. The second may require additional validation. The third should trigger a fresh policy decision, and possibly human approval.

This doesn't mean interrupting every harmless operation. It means defining meaningful boundaries where the consequences change.

Make Decisions Explainable

Allowing or blocking an action is only part of the control.

The system should also record why the decision was made:

Which agent and user initiated the action
Which tool and resource were involved
What context was evaluated
Whether the action was allowed, denied, or held for approval
Which policy or condition determined the outcome

That record gives operators something they can review later. It also helps distinguish a legitimate blocked action from a misconfigured policy.

Build Security Around Agent Behavior

The practical shift is simple:

Don't authorize an agent once and trust the rest of its execution.

Authorize each meaningful step against the agent, user, tool, resource, and current context. When the plan changes, evaluate again. When the impact increases, raise the control level.

That is how autonomous execution stays bounded without making every task unusable.

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The Dangerous Moment for an AI Agent Is When Its Task Changes

AXEC — Tue, 27 Oct 2026 11:31:25 +0000

Most AI agent permissions are approved at the start of a task and forgotten when the task expands.

That’s where the control breaks.

A support-summary agent may begin with permission to read selected customer records and produce an internal summary. The original request is narrow. The authority makes sense.

Then the workflow changes.

The agent is asked to include a spreadsheet export, update a ticket, or investigate a related account. The task sounds like a small extension, but the required actions are different. If the agent keeps its original access without a new check, the system is treating a new task as if it were the old one.

That’s a design mistake.

Authority should follow the task

An agent’s permission should be bounded by four questions:

What task is it performing?
How long should that authority remain active?
Which resources can it access?
Which actions can it take?

For the support-summary example, the boundary might be:

Read selected support records
For one hour
Only within the assigned account set
No export, modification, deletion, or external sending

This is more useful than granting the agent broad “support access” and relying on the prompt to keep it well behaved.

Prompts describe intent. They don’t enforce authority.

The control point is the transition

The important security event isn’t only the first authorization. It’s the moment the agent moves from one kind of work to another.

A runtime control should re-check the request when:

A new resource is introduced
The action changes from reading to writing
Data is prepared for export or external delivery
The task runs beyond its original time limit
A user asks the agent to investigate something outside the initial scope

The agent may still be legitimate. The new action may still be reasonable. But legitimacy should not be assumed from the first approval.

A practical test for an AI agent is simple:

If the task changed, would the permission change too?

If the answer is no, the system is probably carrying authority farther than the original decision justified.

Expiration is part of the design

When the work is complete, access should end. If the workflow fails, times out, or is abandoned, access should still end.

That means permissions need both a task boundary and an expiry time. A resource boundary limits where the agent can go. An action boundary limits what it can do. Expiration prevents yesterday’s approval from becoming today’s standing access.

For founders and enterprise AI teams, this is the implementation detail worth inspecting: not whether an agent has permission, but how that permission is issued, re-checked, and revoked during execution.

Secure autonomy depends on making authority temporary, specific, and easy to withdraw.

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AI Agent Permissions Should Expire Like Temporary Access

AXEC — Tue, 27 Oct 2026 11:31:04 +0000

An AI agent shouldn’t keep access after the task that justified it is finished.

Start With the Task

Permissions should begin with a clear job, not a broad role.

Before an agent can access a system, define:

What task it needs to complete
Which records or resources it can read
Which actions it may take
Which actions are out of bounds

A support-summary agent may need to read selected customer records. It doesn’t need permission to export those records, change account details, or send messages.

The narrower the task, the easier it is to review the agent’s actions.

Set a Boundary Around Authority

Access should have limits beyond the task itself.

Set:

An expiry time
A resource boundary
An action boundary
A clear owner for the permission

For example, an agent could read selected support records for one hour to prepare an internal summary. During that window, it cannot modify the records or export the underlying data.

That gives the agent enough authority to work without leaving a permanent door open.

Re-check When the Task Changes

Agent work rarely stays perfectly linear. A request can expand, a tool can suggest a new action, or the agent can encounter information outside its original scope.

That’s when the authorization decision should be made again.

If the support-summary agent is asked to update a customer record, its original permission should not silently cover that new action. The system should pause, check the policy, and require a new approval when needed.

A change in task should trigger a change in authority.

Revoke Access When the Work Ends

Expiry is useful, but completion should also matter.

When the task is finished:

Revoke temporary access
Record what the agent accessed
Record which actions it took
Close any unused permissions

This keeps authority tied to active work instead of allowing it to linger because nobody remembered to remove it.

The goal isn’t to slow agents down. It’s to make their authority understandable, limited, and accountable.

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The Dangerous Part of Agent Access Is What Happens After the Task

AXEC — Tue, 27 Oct 2026 11:24:05 +0000

An AI agent should never keep access simply because nobody remembered to remove it.

Start With the Task

Access should begin with a specific job, not a broad role.

For a support-summary agent, the scope might be:

Read selected support records
Summarize recent conversations
Work for one hour
Access no export, delete, or modification functions

That definition gives the agent enough authority to complete the task without turning temporary work into permanent access.

Put Boundaries Around Authority

A permission should have more than a name. It should have limits.

Set boundaries for:

Time: When does access expire?
Resources: Which records, systems, or folders can the agent reach?
Actions: Can it read, write, export, or approve?
Purpose: What task is the access meant to support?

The narrower the task, the easier it is to review and control.

Re-check When the Work Changes

Agent workflows rarely stay perfectly linear.

A summary task may lead to a request to update a ticket, search another system, or send information to someone else. That change should trigger a fresh authority check.

The original approval was for the original task. It shouldn’t automatically cover every next step the agent discovers.

A useful control is to pause when:

The target resource changes
The requested action becomes more sensitive
The task moves beyond its original purpose
The agent needs a new tool or data source

This keeps the agent from treating momentum as permission.

Make Expiry the Default

When the work is complete, access should end without someone having to remember a cleanup step.

Temporary authority can be tied to:

A fixed expiry time
Completion of a defined task
A change in user approval
A policy decision or risk event

For the support-summary example, the agent might read selected records for one hour, then lose access automatically. It never needs export or modification rights because those actions are outside the task.

That is a better operating model than granting broad access and hoping it gets revoked later.

The Practical Test

Before giving an agent access, ask four questions:

What exactly is it doing?
What is the smallest amount of access it needs?
What happens when the task changes?
When, precisely, does the authority end?

If those answers aren’t clear, the agent is probably receiving a role instead of a permission.

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An AI Agent Should Not Inherit Your Entire Permission Set

AXEC — Tue, 27 Oct 2026 11:23:49 +0000

Copying a user's permissions into an AI agent is a security shortcut that creates authority nobody can clearly explain.

User identity is not agent authority

A person may be allowed to access many systems because of their role. An agent working on one task should not automatically receive the same reach.

The real question is not, “Who started this request?”

It is, “What is this agent allowed to do for this specific task?”

That distinction matters when an agent can call tools, access records, or trigger business actions without waiting for a person at every step.

Start with a narrow task scope

Consider an agent helping a support team summarize customer feedback.

It may need to:

Read selected support records
Group recurring issues
Draft a summary for review

It should not automatically be able to:

Export the entire support database
Modify billing information
Change account permissions
Send messages to customers

The agent’s authority should match the work, not the initiating employee’s entire permission set.

Put limits around delegated authority

A useful authorization policy should answer a few practical questions:

Which data can the agent access?
Which tools can it use?
Which actions are prohibited?
How long does the authority remain valid?
What happens when the task changes?

Time limits matter. An agent approved to analyze a support queue this morning should not retain the same access indefinitely.

Tool limits matter too. Permission to read a record does not imply permission to export it, edit it, or share it elsewhere.

Recheck when the task changes

Agents often move from one step to the next. That transition is where broad permissions become dangerous.

An agent may begin by summarizing support records, then decide it should open a billing system to investigate a pattern. That new system, new data, and new action should trigger another authorization check.

If the request falls outside the original scope, the agent should pause, ask for approval, or stop. Continuing automatically is not autonomy. It is uncontrolled delegation.

Make the decision explainable

For sensitive actions, teams need more than a record showing that a user initiated the request.

They need to reconstruct:

What the agent was asked to do
Which authority was delegated
Which tools and data were available
Why the action was allowed or blocked
Whether a person approved the outcome

That evidence helps security and governance teams understand what happened without relying on assumptions.

An AI agent should have enough authority to complete its task, and no more.

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Prompt Injection Is a Permission Problem, Not Just a Prompt Problem

AXEC — Tue, 27 Oct 2026 11:17:22 +0000

An AI agent does not become safe simply because its instructions are well written.

Untrusted Text Can Change the Plan

Prompt injection happens when an agent encounters text that tries to redirect its behavior.

That text might appear in:

A support ticket
An uploaded document
A web page
An email
A knowledge-base article

The agent may be asked to summarize the content, but the content itself might contain instructions such as “ignore previous rules” or “send this data elsewhere.”

The key mistake is treating every piece of text as equally authoritative.

Data should inform an agent’s reasoning. It should not automatically gain the power to redefine the agent’s permissions.

Permission Must Come From Policy

A safer design separates three things:

What the user asked for
What the agent is allowed to do
What the surrounding data is trying to make the agent do

Consider a support agent asked to summarize customer complaints.

It may be allowed to:

Read selected support records
Group recurring issues
Draft an internal summary

It should not automatically be allowed to:

Export the full customer database
Change billing records
Send external messages
Reveal sensitive account information

Even if an uploaded ticket tells the agent to perform one of those actions, the instruction should fail the policy check.

Check Actions at Runtime

A permission decision made at the beginning of a task may not be enough.

Agents encounter new tools, data, and instructions as they work. Each meaningful action should be evaluated against the current task and its approved scope.

Useful runtime checks include:

Is this action necessary for the stated task?
Is the target system allowed?
Does the data contain untrusted instructions?
Is the action within the agent’s time and authority limits?
Would the outcome create a material business or security risk?

This turns prompt-injection defense into an authorization process rather than a contest between competing prompts.

Sensitive Outcomes Need a Pause

Some actions should not happen automatically, even when they appear technically permitted.

Require deliberate human approval before an agent:

Changes financial or billing information
Sends a message outside the organization
Exports sensitive records
Makes an irreversible system change
Acts outside the original task scope

The approval should show what the agent intends to do, which data it will use, and why the action is being requested.

That gives the reviewer something concrete to approve or reject.

The Goal Is Bounded Autonomy

Prompt injection will remain a practical risk wherever agents process untrusted content.

The answer is not to eliminate autonomy. It is to make autonomy bounded:

Untrusted text can influence analysis, not authority
Policies define permitted actions
Runtime checks evaluate each meaningful step
Humans approve sensitive outcomes
Audit evidence records the request, decision, approval, and result

An agent is safer when it cannot turn a piece of text into permission.

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Before AI Agents Go Autonomous, Define Who They Are Allowed to Be

AXEC — Tue, 27 Oct 2026 11:17:08 +0000

As AI agents move from generating answers to taking action, enterprises need a clearer model of identity, authority, approval, and evidence.

The User Is Not the Agent

When an employee asks an AI agent to complete a task, it may be tempting to treat the agent as an extension of that employee’s identity.

That approach creates an authorization gap.

The initiating user may be allowed to access certain systems, but the agent is the entity that interprets instructions, selects tools, retrieves information, and performs actions. Those are separate steps with separate risks.

Consider a simple request: “Prepare a supplier review and update the procurement record.”

The user may be authorized to request the review. But several questions remain:

Which agent handled the request?
Which systems did it access?
What information did it use?
Was it allowed to update the procurement record?
Did it make decisions beyond the user’s original intent?

Without a distinct agent identity, these actions can collapse into the user’s permissions. That makes it difficult to apply precise controls and even harder to determine what happened later.

An agent should have an identity that is separate from the human who initiated the task. That identity should be visible in authorization decisions, tool calls, and audit records.

Delegated Authority Must Be Narrow

Giving an agent an identity is only the starting point. The next question is what that identity is allowed to do.

Delegated authority should be task-specific, time-bound, and limited to the resources required for the job. It should not mean giving an agent broad access simply because the initiating user has broad access.

For example, an agent asked to summarize customer feedback may need permission to read a defined set of support records. It may not need permission to modify those records, export them, or access unrelated financial systems.

This distinction matters because agent tasks often cross several boundaries. An agent may begin with a harmless request, then encounter information that suggests another action would be useful. It may discover a new tool, receive an instruction from a document, or infer a follow-up step that was never explicitly approved.

Broad access turns these possibilities into uncontrolled authority.

A better model defines the boundaries before execution:

Which systems and data may the agent access?
Which tools may it use?
Which actions may it perform?
Which actions are prohibited?
How long does the authority remain valid?
What happens when the task changes?

Delegation should preserve the original purpose of the request rather than becoming a general-purpose permission grant.

Authorization Must Continue During Execution

Traditional authorization often happens at the beginning of a session. An agentic workflow requires decisions throughout the execution.

The context can change as an agent works. New data may appear. A tool may return an unexpected result. The agent may reach an action with greater business impact than the initial request suggested.

That is why runtime authorization is essential.

Before each meaningful step, policy should be able to evaluate the agent, the initiating user, the requested action, the target resource, and the current context. The decision may be to allow the action, deny it, or require additional approval.

This approach is more precise than assuming that an initial approval covers every later action.

For instance, an agent may be authorized to draft a contract summary but not send a contract externally. It may be allowed to identify a payment anomaly but not initiate a transfer. It may be permitted to prepare a production change but not deploy it.

The difference is not whether the agent is generally trusted. The difference is whether the specific action falls within the authority delegated for the task.

Human Approval Is a Control, Not a Failure

Some actions should pause for human approval, particularly when they create financial, legal, operational, or reputational consequences.

Human approval does not need to interrupt every step. Used deliberately, it can act as a control point for sensitive actions while allowing lower-risk work to proceed automatically.

An agent might research a renewal, prepare the recommended change, and identify the affected systems. The final modification could require an authorized person to review and approve it.

The important detail is that approval should be tied to a specific action and context. A generic “approved” status is not enough. The record should show what the agent intended to do, what information informed the action, who approved it, and whether the action changed before execution.

This creates a useful boundary between preparation and commitment. The agent can reduce operational effort without receiving unrestricted permission to make consequential decisions.

Prompt Injection Is an Authorization Problem

Prompt injection is often described as a problem of malicious instructions entering an agent’s context. It is also a problem of authority.

An agent may encounter instructions in a document, web page, message, or tool response that conflict with the user’s request or the organization’s policy. If those instructions can influence the agent’s behavior, they may attempt to redirect the agent toward actions it was never authorized to perform.

The right response is not simply to ask the model to “ignore” suspicious text. The surrounding controls must determine which instructions are trusted, which sources are untrusted, and whether the requested action is permitted regardless of what the content says.

Policy enforcement and runtime authorization provide that boundary. Even if an agent is influenced by untrusted content, it should not be able to access a prohibited system, disclose restricted information, or perform a sensitive action without the required authority and approval.

Prompt-injection protection therefore belongs inside the broader authorization model, not as an isolated feature.

Evidence Must Reconstruct the Execution Chain

When an agent takes action, an organization needs more than a final success or failure message. It needs evidence of how the action occurred.

A useful audit trail should connect the full execution chain:

The original request
The initiating human identity
The agent identity
The authority delegated for the task
Policy decisions made during execution
Human approvals
Data and tools accessed
Actions attempted and completed
The resulting outcome

This evidence supports investigation, governance, and operational learning. It can help answer whether the agent followed its assigned purpose, whether a policy decision was applied correctly, and where an unexpected action entered the workflow.

For enterprises adopting autonomous systems, the goal is not to eliminate human responsibility. It is to make responsibility visible and enforceable as work moves between people, agents, tools, and systems.

AXEC addresses these control requirements through agent identity, delegated authority, runtime authorization, policy enforcement, human approvals, prompt-injection protection, and audit evidence.

Before agents become more autonomous, organizations should define who those agents are allowed to be, what they are allowed to do, and what evidence they must leave behind.

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Authorization Cannot Stop at the Start of an Agent Task

AXEC — Tue, 27 Oct 2026 11:10:38 +0000

A permission granted at the start of an agent task can become wrong before the task is finished.

An agent may begin with approval to investigate a production issue. During execution, it finds a new document, calls another tool, or changes its plan. Each discovery can expand what the agent is able to do.

That means the original authorization is no longer enough.

Consider an agent helping prepare a production configuration change. It may be allowed to read the relevant logs, compare configuration files, and draft a proposed change.

It should not automatically gain permission to deploy that change.

The deployment step has a different risk profile. It affects a different resource, uses a different tool, and may require a different level of human accountability. Treating it as part of the original approval creates a gap between what the user authorized and what the agent eventually does.

A runtime authorization decision should evaluate:

The agent identity
The requesting user
The tool being called
The resource being accessed
The current context
The action the agent is attempting

The result can be allowed, denied, or held for approval.

The important part is timing. The check needs to happen at each meaningful transition, not just when the task starts.

The plan change is the control point

Agents don't always follow their initial plan. A new file can reveal a dependency. A tool can return data that suggests a more effective action. A user can ask the agent to continue after the original scope has changed.

That plan change should trigger a new authorization decision.

For the production example, the flow might look like this:

The agent reads approved operational data.
The agent drafts a configuration change.
The agent requests deployment access.
Policy evaluates the new action.
The action is blocked or sent for human approval.

The agent can still be useful without receiving unrestricted authority. It can do the analysis and prepare the work while the higher-risk action remains bounded.

A practical buyer test

Ask to see what happens when an agent moves from reading to changing.

Then ask:

Does the policy evaluate the action at runtime?
Can it distinguish drafting from executing?
What happens when the agent's plan changes?
Is approval tied to the specific action and resource?
Is the reason for the decision recorded?

If the answer is only “the user approved the task,” the control is probably too broad.

For AI agents, authorization isn't a checkbox at the start of execution. It's a continuing decision about whether the next action is justified in its current context.

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An AI Agent Needs Its Own Identity to Be Governed

AXEC — Tue, 27 Oct 2026 11:10:20 +0000

An AI agent should not inherit authority from the employee who started its task.

That shortcut makes the system look simple in the access-control diagram. It also hides the most important question in an investigation:

Which actor actually made this decision and called this tool?

An employee may ask an agent to “prepare the customer renewal.” The agent might then retrieve account data, query pricing systems, draft a proposal, and initiate a workflow. Those actions are not the employee’s actions repeated at machine speed. They are actions taken by a delegated system with its own context, tools, and decision path.

If the logs record only the employee, governance loses the agent’s behavior inside the delegation.

The identity chain must survive the task

A governable agent needs an identity that can be carried through three points:

Authorization: which agent is allowed to use which tool?
Execution: what task and policy scope justify the call?
Audit: what did the agent actually do, and under whose delegation?

This does not mean creating an unlimited identity for every software component. It means making the agent a distinct actor within a bounded chain:

Employee request → Agent identity → Task scope → Tool action

Each step should be inspectable.

For example, an agent authorized to summarize internal account data may not automatically be authorized to export that data, modify a customer record, or send an external message. The employee’s permissions should not silently expand the agent’s authority.

Where the control usually breaks

The failure is often in the tool layer.

A team gives an agent access to a shared service account because it is faster than designing delegated permissions. The agent can now call several systems, but the resulting log says only that the service account acted. The original task, the agent version, the requested scope, and the policy decision are missing or disconnected.

That creates two problems.

First, the system cannot reliably prevent an action outside the task. Second, a reviewer cannot reconstruct whether the action was expected, induced by untrusted input, or simply the result of excessive authority.

The practical test is straightforward:

Can your team answer what this agent was allowed to do before it acted, not just what it did afterward?

If the answer depends on reading application logs across multiple systems, identity is already too weak.

Design for bounded, reviewable agency

Start with four controls:

Identify the agent. Give it a distinct, stable identity rather than hiding it behind a human or generic service account.
Bind it to a task. Attach the identity to the request, purpose, and time-bound scope.
Limit its authority. Authorize specific tools and actions, not broad access to an entire environment.
Make the chain reviewable. Preserve the employee, agent, task, policy decision, tool call, and outcome together.

The goal is not to slow every agent down with a human approval. It is to ensure that autonomy remains attributable and bounded.

An agent that cannot be identified cannot be meaningfully authorized. An agent that cannot be authorized cannot be safely trusted.

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The Best AI Agent Audit Trail Reads Like a Timeline

AXEC — Tue, 27 Oct 2026 11:03:32 +0000

A successful AI agent run is not proof that the action was safe.

“Task completed” tells you the outcome. It doesn’t tell you what the agent was asked to do, which authority it used, what policy it encountered, whether a person approved the action, or which tool actually changed the system.

That gap becomes expensive when someone asks a simple question:

Why did the agent do that?

An enterprise audit trail should answer the question as a timeline, not as a pile of disconnected logs.

Follow the execution chain

Consider an agent asked to update a supplier record.

A useful record should connect:

The original request and requesting human
The agent identity and delegated authority
The policy decision at the time
Any approval or exception
The tools called and parameters used
The data returned by those tools
The final action and outcome

This is more than observability. It is evidence of how authority moved through the system.

Without that chain, reviewers are forced to reconstruct events from application logs, identity systems, model traces, and ticketing tools. The evidence may exist, but the decision does not.

Where the control breaks

The most common failure is recording only the final state.

The supplier record changed. The payment workflow advanced. The access request closed. But the audit trail cannot show whether the agent:

Received an instruction from an untrusted document
Used a broader permission than the task required
Skipped a policy check after its context changed
Acted on stale information
Received human approval for the exact action performed

That makes incident review slow and control improvement guesswork.

A buyer evaluating an agent platform should test this before deployment:

Can an independent reviewer reconstruct one action from request to outcome without asking the agent to explain itself?

If the answer is no, the system may be logging activity without producing audit evidence.

The practical design test

For every consequential agent action, ask whether the record preserves three things:

Decision context
What did the agent know, what was it trying to do, and which policy applied?
Execution authority
Which identity, role, or delegated permission allowed the tool call?
Reviewable outcome
What changed, who approved it, and can the action be tied back to the original request?

Capture these decisions as they happen. Do not rely on a final summary generated after execution. Preserve the execution chain in a form that security, platform, and governance teams can understand without reverse-engineering the runtime.

The best audit trail does not merely prove that an agent acted.

It shows whether the action was justified.

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