How does Temporal handle failures during a workflow?

If a failure occurs, Temporal remembers the state and resumes the workflow on another host with the same context, continuing execution from where it left off without the developer writing extra code.

Can you give a concrete Uber use-case where Temporal helped?

Uber used Cadence (Temporal’s predecessor) to manage long-running processes like the Uber loyalty program, where trip events triggered a points workflow that persisted state and could recover from outages or bugs with replay and versioned progress.

What is the role of multi-region namespaces in Temporal Cloud?

Multi-region namespaces allow failover of named spaces to different regions, enabling near-continuity of service during regional outages with minimal disruption.

What is the agentic loop and how does Temporal fit into it?

The agentic loop is where a model uses a context and tools to plan and invoke actions; Temporal provides the durable execution backing to manage the long-running, asynchronous tool invocations and state for these agents.

What is CodeEx and how does it relate to Temporal?

CodeEx describes agents that orchestrate multiple tools in the background to accomplish complex tasks without running code in the foreground; Temporal serves as the execution authority to coordinate those tool invocations reliably at scale.

What is project Nexus in Temporal's roadmap?

Project Nexus is an industry-wide initiative to drive standards for durable RPC and asynchronous tool invocations to better stitch together a swarm of agents; Temporal participates and advocates for cross-vendor collaboration.

How has Temporal's strategy evolved post-2021 in fundraising and growth?

Temporal focused on profitability and customer value, emphasizing solid margins and a durable business model; they prioritized solving real customer needs and building a scalable R&D engine rather than chasing unfettered growth.

Key Moments

Temporal CEO on AI Agents & The Future of Software | Deep Dives with a16z

a16z

Gaming4 min read63 min video

Feb 19, 2026|1,907 views|51

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Key Moments

TL;DR

Durable execution powers long-running AI agents with reliable state and replay.

Key Insights

Temporal provides durable execution: it remembers state, auto-resumes failed executions on another host, and guarantees each task runs to completion without developer code changes.

Real-world Uber use cases illustrate durability at scale: tipping flows migrated from Kafka to Temporal, and long-running loyalty programs kept state in workflows with automatic recovery after outages or bugs.

Agentic shift enables long-lived, specialized agents and coding agents. Temporal is positioned to orchestrate vast swarms of agents, offering reliability, observability, and scalable state management.

Observability and context management are central: workflow traces, auditable histories, and real-time context from diverse sources power better prompts, debugging, and analytics.

Temporals' integration-first strategy and standards focus: plug-ins and collaborations (e.g., OpenAI, Pedantic) to avoid vendor lock-in and encourage an ecosystem of interoperable tools.

Future of agent workflows: industry-wide durable RPC, large payload handling, sandboxed experimentation, and a shift toward internet-scale, API-driven automation.

DURABLE EXECUTION: DEFINED, EXPLAINED, AND ESSENTIAL

Durable execution is Temporal's core promise: it abstracts away the messy state management involved in running distributed, stateful code. When a failure occurs mid-execution, Temporal remembers the entire state and transparently resumes on a different host, continuing exactly where it left off. Developers write business logic without worrying about retries, retries, or failure handling, because Temporal guarantees the end-to-end progression of each operation. This creates a predictable, reliable runtime for complex workflows that span dozens of microservices with varying availability, transforming chaotic real-world processes into orderly, auditable sequences.

UBER, CADENCE, AND THE TIP OF THE ICEBERG: EARLY USE CASES THAT SHAPED A PLATFORM

Temporal’s roots trace back to Uber, where early scale highlighted the need for durable, fault-tolerant orchestration as the company migrated from a monolith to microservices. Early success with Cadence demonstrated how long-running transactions—such as tipping flows and loyalty points—could be modeled as workflows with built-in retry policies and state resilience. A notable episode involved Uber One, where loyalty points were accumulated via trips; a bug rolled out in production, compromising points across users. Temporal’ s ability to replay events and revert to known good states prevented broader damage, underscoring the economic and operational value of durable execution and event-sourced state.

AGENTIC APPS: FROM SHORT-LIVED TASKS TO LONG-LIVED, AUTONOMOUS WORKFLOWS

The conversation frames a shift from prompt-driven, short-lived agent interactions to long-running, autonomous agent loops. Temporal supports the agentic model by providing durability, recoverability, and a robust execution authority behind complex tool invocations. With coding agents and multi-agent ecosystems, developers can orchestrate dozens or hundreds of agents performing asynchronous tasks, learning from history, and scaling without losing track of progress. The platform’s durability is critical when agents operate across time horizons that extend far beyond a single session or human interaction.

CONTEXT MANAGEMENT AND OBSERVABILITY: CONTEXT AS A FIRST-CLASS CURRENCY

A central theme is context management—pulling in real-time data from APIs, Slack, Google Docs, and other sources to feed prompts and drive correct outcomes. Temporal’s event-sourced workflows provide auditable traces, making it possible to see exactly which tool invocations occurred, when, and with which results. This visibility isn’t just for debugging; it becomes a data source for analytics, prompts optimization, and training feedback loops. As agents grow more nondeterministic, robust observability and prompt management become essential guardrails.

SCALING STRATEGIES: INTEGRATION-FOCUSED PLATFORM WITH NEXUS AND OPEN ECOSYSTEMS

Temporal emphasizes an integrations-first approach rather than building a monolithic, single-vendor platform. By partnering with OpenAI’s agent SDK and others, Temporal acts as the execution backbone while other components provide domain-specific capabilities. Projects like Nexus aim to standardize durable RPC and asynchronous tool invocations, inviting industry-wide collaboration. The strategy recognizes that enterprises will require both deterministic, auditable processes for core operations and flexible, interoperable agents for exploratory automation.

THE ROAD AHEAD: INTERNET-SCALE AGENTS, LARGE PAYLOADS, AND A NEW OPERATING MODEL

Looking forward, the world of agentic apps is moving toward a swarm of specialized agents, longer-lived workflows, and a demand for handling large payloads and high-velocity data. Temporal projects to address this include large-payload support and better tooling for sandboxing, prompt management, and observability at scale. The industry may adopt industry-wide standards for durable RPC and agent orchestration, with Temporal playing a central role as the execution authority that keeps complex distributed processes reliable, auditable, and production-ready as automation becomes the new normal.

Mentioned in This Episode

●Software & Apps

●Concepts

●People Referenced

Common Questions

Durable execution guarantees that a function runs to completion even in the face of failures by preserving state and transparently resuming on a different host. Temporal is the open-source platform that provides this as a core value proposition, so developers don't have to implement retry logic or state management themselves.

Topics

Durable Execution Temporal Agentic Apps Long-running Workflows Retry Logic Event Sourcing Multi-region Namespaces Observability OpenAI Aentic SDK Temporal Cloud Nexus

Mentioned in this video

Companies

Temporal

Open-source platform that guarantees durable execution of code, preserving state across failures and resuming where left off without custom code.

Software & Apps

Kafka

Apache Kafka used to process large data streams; used in Uber tipping flow and later replaced by Temporal-based workflows.

OpenAI's Aentic SDK

SDK for agentic AI integration used to connect Temporal as the state manager behind pedantic.

People

Ragu

Ragu K. ( Temporal executive) discussing platform shifts, agentics, and industry standards.

Sarah

Interviewer who references examples and brings up questions during the interview.

Max

Maximilian (one of Temporal's co-founders) discussing the company's evolution and leadership switch.

Products

Cadence

Open-source predecessor platform to Temporal created at Uber to fix durability and state management for long-running workflows.

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