The Invisible Backbone Powering AI Agents: Why Durable Execution Matters
Here's a thought experiment: you ask an AI agent to research a complex topic. It works for three hours, searches hundreds of pages, and compiles a detailed report. Then it crashes.
If this were a simple prompt, no big deal—you'd hit enter again. But this agent just burned thousands of tokens and three hours of compute time. Starting over isn't just inconvenient; it's expensive.
This is the problem that durable execution solves, and it's becoming one of the most critical infrastructure layers in the AI stack.
The Shift From Prompts to Agents
Traditional AI interactions are short and self-contained. You send a prompt, get a response, done. The technology underneath can be stateless because there's nothing to save.
But AI agents are different. They might need to:
- Gather information from multiple sources over minutes or hours
- Make decisions based on partial data
- Wait for external events or human input
- Retry failed operations
- Coordinate across multiple tools and services
These aren't quick interactions. They're long-running workflows where losing progress is costly.
What Durable Execution Actually Means
At its core, durable execution is about guaranteeing exactly-once processing. When a system records your workflow's state at every step, it can recover from failures without duplicating work or losing progress.
Think of it like a restaurant kitchen. On a busy night, orders come in at unpredictable intervals. Stations might go down. Chefs might need to step away. But every order still gets processed in the right sequence and delivered exactly once.
Without durable execution, managing all that chaos falls on the developer. With it, the infrastructure handles the complexity. You write your business logic; the platform guarantees execution.
Real-World Scale
This isn't a theoretical problem being solved in research labs. Durable execution infrastructure already powers some of the largest AI systems in production:
- OpenAI Codex uses it to handle coding agent workflows that run for extended periods
- Snap processes every story through durable execution infrastructure
- Coinbase runs transactions on the same technology
- YUM Brands (KFC, Taco Bell, Pizza Hut) manages orders through these systems
These companies aren't experimenting. They're running mission-critical workloads at massive scale.
Why Agents Need This Specifically
Here's what makes agentic systems different from traditional software: they're non-deterministic. An AI model might make different choices depending on context, and external APIs behave unpredictably.
Traditional software fails predictably. Agentic software fails in unexpected ways. When your coding agent decides to try a different approach mid-task, that's great—but it also means the system needs to track that new state and recover correctly if something goes wrong.
Durable execution handles this naturally. Each decision becomes an event. Each tool call gets recorded. The workflow can always resume from exactly where it left off.
The economics are compelling too. When token costs were high, losing a three-hour agent run was painful. Now that inference has gotten 150X cheaper, the relative cost of failures has dropped—but the absolute cost of long-running agents is still significant, and recovery still matters.
The Missing Piece: Connecting Agents Together
Here's where it gets really interesting. The industry is moving toward swarms of specialized agents—each handling a specific task, coordinating to solve complex problems.
But how do these agents talk to each other? How do you ensure that when Agent A calls Agent B, the call completes reliably, even if it takes hours?
This is the durable RPC problem. It's the next big infrastructure gap, and companies are racing to solve it. The goal: a standard protocol for asynchronous, reliable agent-to-agent communication.
What This Means For Builders
If you're building AI agents today, here are the practical implications:
Don't build recovery logic yourself. Use platforms that handle durable execution. The complexity of recovery is higher than it looks, and you'll get it wrong at scale.
Think about state from day one. What happens when your agent crashes mid-workflow? Define your checkpoint strategy before you need it.
Watch the orchestration layer. As agents become more capable, the infrastructure connecting them becomes more valuable. This is where defensibility lives.
Plan for multi-agent systems. Single agents are impressive. Coordinated swarms are the future. Your architecture should support both.
The Bottom Line
The AI agent era has created a new category of infrastructure needs. Durable execution is the foundation—it's what makes long-running agents practical at scale.
The companies that get this right are already powering the most important AI systems in the world. The rest of the industry is catching up.
If you're building agents, this is the layer you can't afford to ignore.