AI agent optimization closes the gap between being cited in an answer and being selected for an action.
OpenAI’s August 7, 2026 report says workplace users are more than twice as likely to use ChatGPT for completion or creation than people outside work. Yet about 49% of queries remain classified as Asking. The execution layer therefore joins the answer layer; it does not replace it.
The new failure mode
A brand can appear in an AI-generated answer and still be skipped when the user delegates the next step. The agent may understand the brand’s expertise without finding a capability it can safely invoke.
For engineers, the requirement is concrete: expose what the tool does, which inputs it accepts, what permission it needs, what it returns, and how it reports failure.
Define a callable capability
A capability manifest can make those details inspectable before invocation:
{
"name": "create_content_brief",
"description": "Create a content brief for a topic and audience",
"input_schema": {
"type": "object",
"required": ["topic", "audience"],
"properties": {
"topic": { "type": "string" },
"audience": { "type": "string" }
}
},
"permissions": {
"read": ["approved_sources"],
"write": []
},
"result_schema": {
"status": ["completed", "failed"],
"data": "object_or_null",
"error": "object_or_null"
}
}
The exact protocol can vary, but an agent should not have to infer required inputs, permissions, or failure behavior from marketing prose.
Review each capability against five checks:
- Discoverable: Does its description connect a user’s intent to a specific action?
- Callable: Are required inputs structured and explicit?
- Reliable: Is the returned shape consistent enough for the next workflow step?
- Bounded: Does it request only the access needed for that action?
- Safe: Can it report failure without presenting an incomplete result as success?
Separate answer intent from action intent
A page answering “What is this?” serves a different intent from a capability handling “Use this to complete the task.” Supporting delegated work does not mean removing explanatory content. Keep the answer surface, then add an execution surface beside it.
This distinction matters because discoverability alone can earn a mention without earning selection. Callability alone can move the failure into execution. Safe agent use requires the whole path from intent to result.
Measure the handoff
Log each eligible capability decision, including cases where the capability was considered but not selected:
{
"timestamp": "timestamp",
"request_id": "request_identifier",
"capability": "create_content_brief",
"eligible": true,
"selected": true,
"execution_status": "completed",
"fallback_used": false,
"failure_code": null
}
This supports actionable metrics:
- Agent Selection Rate: selected eligible requests divided by all eligible requests.
- Completion Rate: completed executions divided by selected executions.
- Fallback Rate: executions using a fallback divided by selected executions.
If you log only successful invocations, you cannot see whether the capability was skipped during selection or abandoned after execution began.
Keep the tradeoffs visible
A broad promise may look discoverable while leaving inputs, permissions, and outputs ambiguous. A narrower capability is easier to evaluate, invoke, monitor, and fail safely.
The durable approach is additive: keep optimizing content for answers, then give agents a structured and measurable path to action.
Which part of the agent handoff is hardest in your stack today: capability discovery, structured inputs, permission boundaries, completion tracking, or fallback behavior?
📖 Read the full guide → AI Agent Optimization: From Asking to Doing
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