Running AI agents in production exposes serious gaps: LLM timeouts, conflicting agent actions, runaway costs, and zero auditability. Temporal addresses these by providing durable workflow execution as an enterprise-grade control plane for agentic systems. Key capabilities covered include: human-in-the-loop approval gates via Signals, workflow isolation to prevent conflicting agent writes, full execution history for auditing and debugging, versioning APIs for safe in-flight migrations, retry and cost controls at the activity level, and Temporal Nexus for cross-team agent coordination. Four agent patterns are discussed — conversational, event-stream, ambient, and multi-agent — each mapped to specific Temporal primitives that handle their unique requirements.

13m read timeFrom temporal.io
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Table of contents
The many shapes of an agent #The leadership layer: Guardrails, not just guardbumpers #Coordinating humans, not just machines #Change management: Versioning for the AI era #The operational realities: Cost, retries, and long-running agents #Visibility: Knowing what your agents actually did #Context preparation: The data work before the thinking work #Putting it together #
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