Agent audit logs and regression checks a small team can maintain
Record decisions and execution outcomes without raw secrets, then turn synthetic failures into repeatable release checks.
Read the guideThe agent security handbook
Start with one safe, synthetic agent action. Inspect the request, predict the policy decision, and check the result. Then use the handbook to design limits for your own workflow.
Try one synthetic check See the reproducible evidence →The handbook
Clear answers, small examples, and checks you can use with your own stack. Each guide names its limits and primary sources.
Record decisions and execution outcomes without raw secrets, then turn synthetic failures into repeatable release checks.
Read the guideDesign a request budget that accounts for parallel calls, bounded output, retries, and unknown usage before the next model request is dispatched.
Read the guideDesign a review step that authorizes immutable action details once, rejects changed arguments, and handles retries without silently repeating external effects.
Read the guideAn unrun, repeatable experiment design with six synthetic fixtures, an independent answer key, and clearly hypothetical cost-per-success arithmetic.
Read the guideTrace document deletion through chunks, embeddings, caches, jobs and conversation context with a synthetic matrix and explicit limits on removal claims.
Read the guideA buyer evidence checklist that turns broad gateway claims into a bounded workflow, observable dispatch checks, and explicit pilot acceptance criteria.
Read the guideAn operational response worksheet for accidental model-payload disclosure: stop repeat sends, map copies, preserve minimal evidence, and verify recovery without promising recall.
Read the guideA practical trust-boundary walkthrough for AI apps that summarize documents, retrieve knowledge, and propose tool actions.
Read the guideMap an AI workflow to the smallest tools, data scope, identities, and time limits it needs, then verify denied actions with synthetic fixtures.
Read the guideClassify model failures, preserve policy before fallback, and tell users whether work is queued, incomplete, or stopped without inventing success.
Read the guideTrace direct SDK calls, fallbacks, workers, tool traffic, and copied credentials to check which model requests actually cross a gateway policy boundary.
Read the guideLabel synthetic false positives and missed cases, choose narrow responses, and bind temporary exceptions to one reviewed request and current policy.
Read the guideDefine a model-output rendering policy for plain text, Markdown, links and remote images, then check it with a small synthetic fixture.
Read the guideA practical change-review packet for MCP server identity, tool schemas, annotations and runtime effects, with a synthetic release comparison.
Read the guideReplace broad command execution with narrow operations, validate arguments and object access, and verify filesystem boundaries under realistic runtime conditions.
Read the guideA practical admission worksheet for uploaded documents: constrain formats and parser resources, separate remote fetching, and preserve authorization before indexing or model use.
Read the guideMap model-request data, remove unnecessary fields, check endpoint retention, and test redaction with synthetic records.
Read the guideA two-tenant authorization matrix for document retrieval, shared answer caches, delayed workers, and stored model artifacts, using synthetic records only.
Read the guideA working principle
A convincing model response cannot grant itself more access. Keep permissions, approval, and execution checks in systems you control.
How we approach these guidesFrom OraXLabs
Boundary is a prelaunch commercial model router. Its planned checks apply to model requests passing through the router. This handbook covers a broader educational field.
Read the product scope