Control and proof
Agent intent
Agent intent is the one-sentence reason an AI agent states for an action, recorded with the action itself, so anyone reading the record later can tell what the agent was trying to do and judge whether the action fitted that purpose.
What it is
Most logs say what happened: this key called this action at this time. Intent adds why, in the agent's own words, at the moment it acted: "The customer asked by email this morning to cancel their backup add-on." It is a short sentence, written by the agent, attached to the call and stored with the record of it.
Why it matters
- Review. A person reading an audit log can tell a sensible action from a strange one without reconstructing the whole conversation.
- Spotting manipulation. An action whose stated reason does not match the task is a signal, for example a "routine sync" that exports every contact. See prompt injection.
- Approvals. A person deciding whether to let an action run needs the reason as much as the arguments. See human-in-the-loop approval.
- Accountability. Logs that record only identities and endpoints make investigation slow. OWASP's agent cheat sheet asks for agent decisions, tool calls and outcomes to be logged, and for high-risk actions to carry structured context such as the approval and the policy that applied.
How it works
- The tool or API makes a reason a required field on every action, not an optional note.
- The agent writes one plain sentence in its own words, specific to this call.
- The platform stores it on the same record as the action, its inputs' hash, outcome and cost.
- Grouped work, such as one question sent to several systems, shares one intent across every call it makes.
- A retention rule decides how long the free text is kept, because a reason can quote personal data.
Example
do service.ticket.create· intent: "Logging the backup failure the client reported by phone at 9:05 so the on-call engineer sees it."
Six weeks later an auditor asks why that ticket exists. The answer is on the record, next to who created it and when.
Common pitfalls
- Boilerplate. "Performing task" on every call tells nobody anything. Ask for the specific reason.
- Treating intent as proof. It is the agent's claim. A mistaken or manipulated agent can state a plausible reason; permissions and approvals still have to hold.
- Personal data in free text. Reasons quote customers. Keep the text for a defined period, not indefinitely.
- Optional fields. If the reason can be left out, it will be, on exactly the calls you later need to explain.
How actionlake handles it
Every do and ask call must include an intent: one plain sentence, in the agent's own words, saying why it is making the call. A call without one is refused. The intent is stored on the run's row in the ledger; a read sent to several systems at once is one grouped run, and every call under it carries the same intent, as do the runs of a plan. The intent text is cleared after 90 days; the record of the action, with its hashes, ids, cost and outcome, stays. See the trust page.
Questions
Why record an AI agent's intent?
A log of identities and endpoints says what happened. The agent's stated reason says why, which is what a reviewer, an approver or an auditor needs to judge whether the action was right.
Can an agent's stated intent be trusted?
It is the agent's own claim, so no. It is evidence for review, not a control. Permissions, caps and approvals still decide what may run.
How long should agent intents be kept?
For a defined period that suits your audit needs. Intents are free text and can quote personal data, so keep them shorter than the record of the action itself.
Related terms
- AI agent audit logAn AI agent audit log is a record of every action an agent took: which credential ran it, what it did, in which system, with what outcome and cost, and why, kept so a person can reconstruct and verify events later.
- Tamper-evident ledgerA tamper-evident ledger is an append-only record in which each entry stores a cryptographic hash of its own contents together with the previous entry's hash, so any later edit, deletion or reordering breaks every hash after it and becomes detectable.
- Human-in-the-loop approvalHuman-in-the-loop approval is a control that pauses an AI agent's action until a named person approves or rejects it, so high-impact or irreversible actions never run on the model's judgement alone.
- Prompt injectionPrompt injection is an attack in which text that a language model reads, typed by a user or hidden in a web page, email, document or tool result, changes the model's behaviour, and in a tool-using agent it can turn into real actions.
- AI agentAn AI agent is software in which a language model decides its own next steps and tool calls in a loop, using results from its environment, until a goal is met or a stopping condition is reached.
Sources
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