Loose prompts create loose answers
If the prompt only asks for an equivalent, the agent may return a candidate without the evidence trail needed for review.
AI agent replacement prompts
Prompt Codex or Claude Code to prepare the search, then let Partglyph run the governed match.
AI agents can help structure industrial replacement work, but a general chat should not become the technical authority for compatibility. The better workflow is to ask Codex or Claude Code to gather the known fields, call Partglyph, and return review-ready evidence.
Prompting risk
A broad AI search can sound useful while skipping the fields that matter.
The risk is not that Codex or Claude Code is useless. The risk is letting a general search and summary replace the product-family checks that the replacement review needs.
Manual web research burns attention
The user can end up copying catalog fields, checking supplier pages, and comparing rows one at a time inside the chat.
Review language can get too strong
A prompt should ask for candidate evidence and missing checks, not final approval or unsupported compatibility claims.
Better prompt structure
Tell the agent to prepare a Partglyph request, not to guess an answer.
The prompt should collect the source part identity, known fields, product family, and application clues, then use Partglyph to return candidates and review evidence.
Prompt input manufacturer, part number, family, dimensions, rating or application context
Tool action call Partglyph through the MCP workflow when available
Agent output candidate summary, evidence fields, missing checks, next review questions
Prompt workflow
Use a four-step agent prompt for replacement review work.
This keeps the agent useful without asking it to invent technical certainty.
Extract known fields
Ask the agent to pull manufacturer, part number, family, dimensions, material, rating clues, and application notes from your text.
Prepare the request
Ask it to structure the Partglyph input and call the supported product-family matcher where available.
Summarize results
Ask it to return ranked candidates, field evidence, weaker paths, and missing checks in plain review language.
Name next actions
Ask it to list the supplier questions or engineering checks that should happen before the candidate moves forward.
Prompt examples
Use prompts that keep evidence and authority separate.
These prompt patterns are designed to make the agent a reviewer assistant, not a substitute approval engine.
Exact part Prepare a Partglyph request for this manufacturer and part number, then summarize ranked candidates and missing checks. Open exact search Dimensions Use these dimensions and family clues to prepare a replacement review request, then keep dimensions tied to candidate evidence. Open decoder Cross-reference Treat this alternate number as a candidate path. Show the evidence that supports it and the fields still needing review. Open cross-reference Missing specs Separate known fields from unknown fields, then ask Partglyph for candidates that can be reviewed with the current evidence. Open missing-spec page Result schema Return the result as source identity, candidate paths, evidence fields, missing checks, and next questions. Open schema page
Agent workflow proof
The stronger prompt creates a better handoff between the agent and Partglyph.
Codex and Claude Code are useful when they prepare the request, call the governed matcher, and explain the result. Partglyph remains the structured replacement intelligence layer that preserves evidence and credit-tracked history.
Use the agent with the tool
Run the match through Partglyph instead of asking an AI chat to improvise the comparison.
Start with a real part number, dimensions, or supplier clue. Ask Codex or Claude Code to prepare the request and use Partglyph to return structured candidate evidence.