--- title: "Temporal" description: "5 posts about Temporal, summarised, each linking to the original." --- # Temporal > 5 posts about Temporal, summarised, each linking to the original. ## Articles ### [How we built OCA, our AI on-call assistant](https://yomu.fyi/post/how-we-built-oca-our-ai-on-call-assistant.md) - Company: [Ramp](https://yomu.fyi/company/ramp.md) - Author: Richard Marmorstein, Austin Ray - Published: Sep 9, 2026 - Type: Explainer Ramp built OCA, an AI on-call assistant that investigates incidents, shares findings in Slack, answers follow-up questions, and can ask Inspect to prepare code-fix pull requests for human review. In the past four weeks, it assisted with 575 of 1,220 incident-related merged PRs; among incidents with merged fixes, OCA-assisted fixes averaged 37% fewer engineers and about 50% less estimated engineer time, based on Slack activity. Investigations run in a monorepo through Claude Code or Codex, with Temporal managing workflow, while prompts, custom tools, skills, and six one-shot hooks provide successive evidence and reasoning checks. Session-driven testing exposed errors such as misleading one-hour log windows, leading to just-in-time instructions and interim updates that balance accuracy with response speed. OCA remains human-in-the-loop: it has broad read-only access, narrowly scoped writes, and cannot merge changes, although the team is building toward end-to-end routine incident handling. ### [Build durable agents with Temporal and Lakebase](https://yomu.fyi/post/build-durable-agents-with-temporal-and-lakebase.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Sam Ingbar - Published: Sep 8, 2026 - Type: Explainer Long-running personal-loan underwriting agents must preserve completed work, resume after worker failures, tolerate repeated tool calls, wait for reviewers, expose operational state, and retain an audit trail. The reference implementation combines Temporal durable Workflows and Activities with Lakebase Postgres, using Temporal Event History for replay and Lakebase schemas for run status, evidence, review state, messages, and metrics. Deterministic identifiers, constraints, guarded updates, and Postgres upserts make repeated Activity attempts target the same logical records, while Signals carry human decisions and long waits do not occupy workers. A Unity Catalog synced table supplies governed underwriting policy, and optional Change Data Feed publishes operational changes to Delta history tables. The implementation includes a React/FastAPI interface, mocked deterministic providers, OAuth credential refresh, and 21 passing tests, but does not validate production lending, compliance, security, scale, or an observed end-to-end feed. ### [Closing the Gap Between Agentic Promise and Impact with Industry Leading Retrieval](https://yomu.fyi/post/closing-the-gap-between-agentic-promise-and-impact-with-industry-leadi.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Aug 13, 2026 - Type: Announcement McKinsey projects AI agents will generate $2.9 trillion annually; a WRITER survey finds 97% of executives have deployed agents, yet only 29% see significant ROI due to data accuracy issues. MongoDB announced capabilities embedding benchmark-leading retrieval directly into its operational database, closing the gap between agentic promise and impact. Automated Embedding in MongoDB Atlas powered by Voyage AI, now GA, uses the top-ranked Voyage 4 model family for accurate retrieval without separate pipelines, as demonstrated by MedINT. The Atlas Embedding and Reranking API and voyage-code-4 model, designed for agentic coding retrieval, extend accurate retrieval to any stack and coding agent workloads at low cost. $vectorSearch in Atlas Stream Processing enables agents to retrieve accurate data in motion, and a joint MongoDB-Temporal reference architecture provides a forkable starting point for durable execution on live data. ### [What we’ve learned building cloud agents](https://yomu.fyi/post/what-we-ve-learned-building-cloud-agents.md) - Company: [Cursor](https://yomu.fyi/company/cursor.md) - Author: Josh Ma - Published: Jun 2, 2026 - Type: Explainer Cloud agents have evolved from local-agent extensions into systems running on dedicated virtual machines, with isolated environments, dependencies, network access, parallel execution, and unattended long-running work. That shift makes the development environment a product: reliable execution increasingly depends on rebuilding tools, VM hibernation and resumption, image checkpointing and forking, integrations, and controlled access to secrets and networks. After a fragile work-stealing design produced roughly one 9 of reliability, the team migrated the agent loop to Temporal, whose retries and scheduling across failures helped exceed two 9s; Temporal now handles more than 50 million actions daily across over 7 million workflows. Decoupling agent-loop, machine, and conversation state enables independent pod lifecycles, asynchronous subagents, and retry-aware streaming, while shorter workflows simplify upgrades. The next goal is self-healing environments that can report and resolve missing secrets, blocked network access, or other obstacles. ### [Turbocharging GrabUnlimited with Temporal](https://yomu.fyi/post/turbocharging-grabunlimited-with-temporal.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Michel Parreno - Published: Dec 12, 2024 - Type: Problem & solution Grab’s GrabUnlimited flagship membership program struggled as subscribers grew by over 1000% from January 2022 to June 2023. Its original design used a daily cron job for renewals, Amazon’s SQS to distribute state handlers, and five-minute Redis locks, but outages, corrupted states, concurrent cancellation and renewal, missed benefits, and non-idempotent retries created operational problems. The team migrated the service to a Temporal workflow-based system, enhancing its ability to handle millions of subscribers with greater efficiency and resilience. The source reports an 80% reduction in open production incidents and says the change improved the membership experience for users. This first part focuses on designing a robust and scalable membership system, while migration challenges and lessons are reserved for a subsequent installment.