Versioning and Rollback for AI Agents
Version AI agent workflows like code, diff changes between snapshots, and roll back safely when a new version causes regressions or runaway costs in production.
AI agent security, identity, governance, cost, and the engineering behind the control plane.
Version AI agent workflows like code, diff changes between snapshots, and roll back safely when a new version causes regressions or runaway costs in production.
Agent loops resend the same context every step, which is what prompt caching is for. How to structure prompts for cache hits, what breaks them, and the risks.
A dead-letter queue nobody watches is where work gets forgotten. What belongs in a DLQ, how to alert on it, and how to replay safely once the cause is fixed.
A practical incident response runbook for AI agent breaches: contain damage, revoke credentials, investigate with tamper-evident audit trails, and recover.
Over-broad MCP tool permissions give attackers an amplified attack surface. Learn the failure modes and control classes that shrink the blast radius.
An admin action one person can both request and approve is not controlled. Where maker-checker belongs in an AI platform and how self-approval sneaks back in.
Public agent benchmarks help narrow a shortlist but mislead as a selection criterion. What they measure, how contamination distorts them, how to test your own.
Detect and redact PII before it reaches AI models or persists in logs — covering entry points, detection techniques, redaction strategies, and compliance.
Agents that transact need a credit wallet with reservations, atomicity, and idempotency. The ledger design that makes agent payments auditable.
At-least-once delivery plus agents that take real actions means duplicate side effects. Where idempotency keys belong, why retries multiply cost, safe replay.
How signed trust manifests and scoped admission controls let organizations share AI agents across boundaries without exposing data or credentials.
A staged rollout playbook for AI agents: inventory risk, run a scoped pilot with guardrails in place, define go/no-go criteria, and expand on evidence.
Langfuse's open-source LLM engineering category explained — tracing, evals, and prompt management — and where governance requirements go further.
How agent trust scoring models aggregate identity, behavior, and attestation signals into a runtime gate that controls what autonomous agents can do.
Kong AI Gateway's platform-extension model explained, how it handles agent and LLM traffic, and when a dedicated governance layer is the better fit.
HiddenLayer's model security and AI detection category explained, how it differs from agent runtime authorization, and how to evaluate alternatives.
Contain what AI agent tools can do: tool-level scoping, allow-lists, dry-runs, and human approval gates for high-consequence irreversible actions.
Helicone's open-source LLM observability category explained, what it covers, and how to evaluate alternatives for cost tracking and tracing.
Design abuse-resistant rate limits for AI agents: choose the right unit, window shape, and enforcement scope to protect costs and downstream systems.
Choose the right enforcement action for AI agent guardrails — block, redact, or warn — and understand the fail-open vs fail-closed security trade-off.
Descope and WorkOS's identity-platform category explained, how it extends toward agentic identity, and where a dedicated agent governance layer differs.
Cloudflare AI Gateway's edge-native model explained, when it fits, and how self-hosted, platform-extension, and governance alternatives compare.
CalypsoAI's GenAI validation and inference-security category explained, the evaluation criteria that matter, and when to look at a different layer.
Arize Phoenix's open-source evaluation and tracing category explained, and how it differs from runtime policy enforcement for production agents.