Agent Intake Forms: Turning Messy Requests into Reliable Runs

Most agent failures start before the model even runs. The request is vague, missing fields, or loaded with unspoken assumptions. Then the agent guesses, and the rest of the workflow pays the price.

The fix is boring and powerful: a well-designed intake form. Not a long questionnaire, but a clear schema that turns messy intent into predictable inputs.

This guide shows how to build intake that makes agents reliable without adding friction.

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TL;DR

Good intake is the cheapest reliability win. Turn free-form requests into structured fields, always include success criteria and constraints, and use defaults so people can submit in seconds. When requests are ambiguous, route them to humans by design.

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Why intake matters more than prompts

Prompts are downstream. If inputs are unclear, no prompt can rescue the run. You see the symptoms immediately: extra tool calls to "figure it out," higher latency, inconsistent outputs, and users who stop trusting the system.

Intake is the opposite of guesswork. It is the contract between the person and the agent. When you get the contract right, the rest of the system becomes cheaper to run and easier to evaluate.

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Start with the outcome sentence

Every request should reduce to one line:

"When I give the agent X, it will produce Y within Z minutes, and it will never do A."

This sentence becomes the anchor for every field you collect. If a field does not help define X, Y, Z, or A, remove it. If you cannot write this sentence, your agent is still a project, not a product.

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Required vs optional: the adoption trap

The fastest way to kill adoption is to demand too much input. Most teams over-collect because they are afraid of missing context. The better approach is to keep the required set small and add optional fields only when failures reveal a real gap.

Required fields should be the minimum that makes the outcome unambiguous:

• Task type
• Primary input
• Destination or output format
• Risk level or approval rule

Optional fields can capture preferences without blocking the request:

• Tone or style
• Priority
• Audience or persona

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Convert free text into controlled choices

Whenever you can, use dropdowns, toggles, or single-select options. This is not about locking users in; it is about creating stable branches the agent can depend on.

Examples:

• Output format: "Email draft", "Summary bullets", "CRM update"
• Audience: "Internal team", "Customer", "Executive"
• Risk level: "Draft only", "Suggest actions", "Execute"

Fewer branches means fewer prompt variations and fewer edge cases to test.

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Bake in constraints up front

Constraints should be part of intake, not a last-minute prompt line. They are easier to enforce when they are structured:

• "Do not email customers directly"
• "Only use sources from this folder"
• "Limit output to 200 words"

Treat constraints as a first-class field so they can be validated by tooling and audited later. If you want a fuller guardrail playbook, see /posts/agent-reliability-drilldown.

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A practical example: support triage intake

Here is a real-world intake that works well for a support agent:

• Task type: "triage-support"
• Input: customer email body
• Output format: "summary + suggested category + draft reply"
• Audience: "customer"
• Risk level: "draft only"
• Constraints: "no refunds, no cancellations"

Notice what is missing: no long background, no internal jargon, no optional paragraphs. The intake gives the agent enough to run, while preserving safety through the risk setting.

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Add a missing-info path

Some requests will always be incomplete. The intake should define what happens next:

1. Ask a single clarifying question
2. Route to a human
3. Stop and return a checklist

This prevents the agent from inventing details just to keep moving.

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Store intake as a request object

Treat intake as a schema, not as a prompt. Example:

{
  "task": "draft-support-reply",
  "input": "Customer email body",
  "output_format": "email_draft",
  "audience": "customer",
  "risk_level": "draft_only",
  "constraints": ["no pricing changes", "no account closures"]
}

This is not just for the agent. It is for logging, replay, and auditing. If you are new to structured tool design, pair this with /posts/structured-tooling-and-ontologies.

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How intake evolves over time

Your first version will be wrong. That is normal. The key is to evolve intake based on actual failures:

• If the agent keeps asking the same question, add a field.
• If the agent makes the same mistake, add a constraint.
• If users skip a field, move it to optional or replace it with a default.

Intake is not a static form. It is a feedback loop.

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A simple way to test intake quality

Pick five real requests from your team and run them through the form. Then review:

• How many times the agent had to ask for clarification
• How often reviewers had to correct the output
• Whether the output matched the stated goal

If you see the same missing detail twice, add a field or a default. This is the fastest way to make intake feel "obvious" to users.

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Common intake mistakes

Too many fields is the obvious one, but there are subtler failures:

• A vague "anything else?" field that invites chaos
• No risk setting, which silently defaults to full autonomy
• No output definition, so the agent cannot know what "done" means
• A mismatch between the intake and the tool permissions

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A lightweight intake template

If you are starting from scratch, this is enough to see immediate improvements:

1. Task type (dropdown)
2. Primary input (text or file)
3. Output format (dropdown)
4. Audience (dropdown)
5. Risk level (draft, suggest, execute)
6. Constraints (checkboxes + optional text)

Build this once and reuse it for every workflow. It makes agents feel less magical and more dependable.

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Summary

Agent reliability starts at the door. A clean intake form turns messy intent into structured requests, reduces tool churn, and keeps outputs consistent. Keep it short, use defaults, and always define success and constraints. It is the fastest way to make an agent feel dependable.