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Give a research agent an evidence contract and a finish line

Define the decision, approved sources, weak-evidence flags, output format, and stop condition before a long-running agent begins.

August 13, 2026 9 min read By Pierre Bradshaw
A research agent following an evidence path from approved sources to a one-page decision brief.
Quick Scan

What matters today

Copy a long-horizon research prompt that controls sources, flags weak evidence, and delivers a one-page decision brief with a clear finish line.

Format PRO TIP
Audience Executives using AI at work
Time 9 min read
Topic Agents

Key points

  • Long research runs need a specific decision, approved sources, evidence rules, and an observable finish line.
  • Every material claim should cite a source and location; weak, single-source, conflicting, and missing evidence should remain visible.
  • A one-page decision brief should state the recommendation, confidence, open questions, reversal condition, and next owner.
  • A claim ledger preserves traceability without turning the decision brief into a research transcript.
  • Research permissions should remain read-only until a separate approval authorizes an external action.

The executive summary remains public for search engines and AI answer systems. Subscriber-only sections are marked separately on the page.

Article roadmap

What you will learn

  1. A research prompt for long runs in Grok Bot, Claude Cowork, or ChatGPT Work

  2. How an evidence contract controls sources, conflicts, freshness, and confidence

  3. How to turn a broad goal into the minimum set of decision questions

  4. What a one page brief must contain before the agent can stop

  5. How to test the result without rereading the entire research trail

Jordan, a strategy director, asks an agent to compare three payroll vendors before Friday. Six hours later, the agent has opened dozens of pages and produced a polished market overview. The brief never answers the decision that matters: which vendor can support the company's countries, security requirements, and launch date within budget.

All that activity hides the failure. The assignment gave the agent a subject, but no evidence boundary or finish line. It spent six hours circling the decision instead of answering it.

A useful assignment reads like a research contract. It names the decision, limits the evidence, keeps uncertainty visible, and describes the artifact that must pass review. That contract works in Grok Bot, Claude Cowork, or ChatGPT Work even though each product handles permissions differently.

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Bottom line

The value of Give a research agent an evidence contract and a finish line is repetition. Run it on one real task, save the version that works, and turn the result into a small weekly habit instead of another one-time AI experiment.

About the author

Pierre Bradshaw Founder, PromptHacker.ai

Pierre has spent 25+ years building growth systems across fintech, real estate, lending, campaigns, and AI workflows, with machine-learning work dating back to 2012.

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