The Invisible Infrastructure Powering AI Agents
When you interact with an AI agent, something invisible does most of the work.
That something is infrastructure—specifically, durable execution systems that let agents survive failures, pause mid-task, and resume without losing progress. This layer is becoming essential as AI moves from simple prompts to complex, multi-step agentic workflows.
The Problem With Simple Interactions
Traditional AI interactions are short and self-contained. You send a prompt, get a response, done.
Agents are different. They might need to:
- Gather information from multiple sources
- Make decisions based on partial data
- Wait for external events
- Retry failed operations
- Run for hours or days
If your agent fails midway through a complex workflow, starting over from scratch isn't acceptable. That's where durable execution comes in.
What Durable Execution Provides
Durable execution guarantees exactly-once processing. Even if systems fail, the workflow resumes where it stopped—no duplicate work, no lost progress.
This matters enormously for AI agents because agentic loops are inherently long-running and failure-prone. Network calls fail. APIs return errors. External services go down.
Without durable execution, agents either need complex manual error handling or they lose work when things go wrong.
Real-World Scale
This isn't theoretical. Major companies rely on durable execution infrastructure for production AI systems:
- OpenAI Codex uses it to handle coding agent workflows
- Snap uses it for story processing at scale
- Coinbase relies on it for transaction handling
These aren't experiments. They're critical systems handling real user traffic.
The Architecture Shift
The underlying shift is from short interactive prompts to long-running asynchronous loops.
Early chatbot interactions measured response time in seconds. Agent workflows measure execution time in minutes, hours, or longer.
That changes everything about how you build and operate AI systems. You need orchestration, retries, durable state, and observability that traditional approaches don't provide.
What's Missing Now
Despite progress, gaps remain. There's no standard protocol for connecting specialized agents into reliable distributed systems.
The industry needs what people call "durable RPC"—a way for agents to invoke tools and services with the same reliability guarantees that durable execution provides within a single workflow.
Some call this Project Nexus—connecting swarms of specialized agents into systems that can handle production workloads reliably.
The Bottom Line
If you're building AI agents that do real work, infrastructure matters as much as model quality.
The best model in the world doesn't help if your agent loses progress when something fails. Durable execution and the surrounding infrastructure layer is becoming table stakes for production AI.
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