The Outbox Pattern for AI Agents: Making External Side Effects Reliable
How AI agents can persist intended external actions before sending email, webhooks or transactions so retries do not duplicate side effects.
How AI agents can persist intended external actions before sending email, webhooks or transactions so retries do not duplicate side effects.
How AI teams can version prompts, labels, datasets and scoring rules so evaluation results remain comparable across releases.
How AI inference platforms can pause or evict lower-priority GPU work so voice, chat and real-time agents keep predictable latency.
How RAG systems can propagate deletions across source stores, chunks, vectors, summaries and caches instead of removing only the original file.
How AI agents can declare tool scopes, allowed actions and approval requirements in a machine-readable permission manifest.
How long-running AI agents can persist deterministic checkpoints so crashes and retries resume from known state instead of repeating work.
How AI systems can invalidate prompt and context caches when knowledge, policy, permissions or model versions change.
How AI services can protect latency and reliability by deciding which requests enter the system before queues become overloaded.
How to evaluate whether RAG answers cite enough supporting evidence, not just whether at least one citation appears somewhere in the response.
How AI agents can use short-lived credentials for tools and APIs so automation works without permanent secrets or unlimited account access.