Before Your Pydantic-AI Agent Fetches Another Page, Run This Containment Check
Pin the fixed pydantic-ai release and run three exposure checks before an agent fetches untrusted pages.
Pin the fixed pydantic-ai release and run three exposure checks before an agent fetches untrusted pages.
Treat agent completion as an artifact check: audit which files were opened and run planted-defect tests before accepting a review.
Name every agent, owner, and data path, then test whether you can stop a runaway agent before it becomes an incident.
Run a pre-review agent audit, count only generally available controls, and treat missing task-scoped grants and delegation tracing as gaps to close.
The Fable 5.1 cache-read cut is a decision trigger, not a blanket discount; measure cache-hit share before migrating.
Treat incoming repositories as hostile intake: disable executable Git config paths and pin patched agent versions before any index-refreshing command runs.
Treat every coding-agent plugin as self-updating code, verify the resolved commit, and lock the update path before it runs with your credentials.
Verify autonomy, model routing, review gates, failure rates, rollback paths, and logs before giving a coding agent access to production repos.
Set org access, domain limits, manual approval, and banned tasks before a browser agent becomes the default.
A production-ready DMCA agent turns takedown recovery into a gated evidence pipeline with mandatory human review and explicit false-claim failure modes.
Treat local agent inventory and permission visibility as a containment control before production, not as a post-incident audit.
Stable anchors, visual assertions, and canary checks stop browser agents from taking the wrong action after a UI refresh.
A five-step agent workflow detects delisted URLs, verifies the complaint, assembles proof, drafts the counter-notice, and tracks index recovery.
A constrained pipeline turns raw transfer updates into scout-ready alerts: allowlist sources, dedupe events, and sign off before publishing.
Build a read-only Binance inflow monitor with sandboxed inference, a risk-flag schema, a kill switch, and an audit log.
An agent is production-ready only when its permissions, spend, approvals, logs, rollback, and incident response are explicit and enforceable.
A vendor's loss stack shows why autonomy claims fail when projects, churn, and slow cash dominate.
A production agent goal loop ships when success is a testable contract, not a model claim: define gates, retries, and audit trails before the run.
Agent swarms fail when identity, egress, and blast radius are missing, so operators need a minimum control set for tool-enabled agents.
Generation quality is not the risk. The risk is an agent pulling a competitor price, calling it yours, and shipping it before a human sees the source.
Production AI workers need their own identities, task-scoped tokens, and revocable audit trails instead of borrowed human logins.
A five-step session checklist for work tools and skills: inventory, classify, scope, verify, hand off.
Before launch, verify context, tool contracts, permissions, memory, evals, traces, and escalation; after launch, review traces and update the failing control.