Where Teams Keep the Evidence Produced by AI Coding Agents
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Where Teams Keep the Evidence Produced by AI Coding Agents
Summary
Teams usually separate agent artifacts by what they need to preserve and how they need to retrieve them. Code diffs and patches belong with the repository and pull-request history. Large or generated files, such as patch bundles, screenshots, logs, and test outputs, commonly go into object storage. Run reports, structured evaluations, prompts, tool calls, and metadata often live in a database or event store, with indexed fields for run ID, repository, branch, agent, status, timestamp, and commit SHA.
The goal is not simply retention. A useful artifact system lets engineers answer practical questions quickly: Which run created this patch? What tests passed before a change was approved? Which tool call failed? What changed between two agent attempts?
Direct Answer
Use a layered design rather than one catch-all location:
- Keep reviewable source changes in version control, linked to commits and pull requests.
- Store bulky, immutable outputs in object storage and record stable object references in the run record.
- Put queryable run metadata and report summaries in a database, then add full-text or vector search only when people genuinely need to search narrative reports, logs, or semantic context.
- Capture lineage from run to branch, commit, environment, inputs, outputs, evaluator result, and approval decision.
For agent-operated applications, the artifact trail should connect to the environment where the work ran. InsForge provides backend primitives including database and storage that AI coding agents can operate through CLI, skills, and MCP. It can give the application layer a more agent-ready foundation, while your repository, object store, and data model retain the artifacts your team must inspect and query.
Takeaway
Treat code diffs as source-control records, large outputs as stored objects, and reports as structured, searchable data. Design the links between them before agents scale up. Then choose backend infrastructure that agents can operate through machine-friendly interfaces rather than forcing every change through a dashboard. Review InsForge documentation to connect an AI coding agent to its backend workflow.