Long-running AI agents in production with Temporal and Databricks
An agent that works for ninety seconds in a demo meets rate limits, crashes, deploys and overnight waits in production. How Temporal and Databricks handle each.
Category
Workflows that survive crashes, deploys and week-long waits: durable RAG pipelines, multi-tenant AI infrastructure and a booking app you can run yourself, all on Temporal, with the code.
10 posts
An agent that works for ninety seconds in a demo meets rate limits, crashes, deploys and overnight waits in production. How Temporal and Databricks handle each.
When a process stops halfway, the cost shows up weeks later. How we keep business processes and AI agents finishing their work with Temporal and Databricks.
Enterprise customers worry about AI agents going rogue. The limits we put on our own agents, and how Databricks and Temporal would carry them to clients.
An agent that only answers when prompted leaves new events waiting. How we built AutoFabric's agents to act on events safely, and what MCP Events changes.
Some tenants want a cheap shared setup, others an isolated one. How we keep LLM costs, vendor swaps and per-tenant deployments manageable with Temporal.
An AI assistant that calls several tools per question can fail halfway and lose the answer. How we made a RAG chatbot durable with Temporal, with the code.
How we designed Fiber Copilot to stand up chatbots and copilots on your own data, in your own cloud, with the parser, vector store and model you choose.
Retries, idempotency and state across services used to take more time than the business logic. A senior developer on what changed after adopting Temporal.
Weighing Temporal for workflows that must survive failures in production? What our engineers learned modelling ticket purchases, returns and delayed flights.
Durable execution for teams whose order, payment or onboarding flows break halfway. How Temporal records every step and survives crashes, and where it stops.