Why Long-Running AI Agents Break on HTTP and How Ably Is Fixing It

Why It Matters

Durable sessions give enterprises reliable, multi‑device AI experiences, unlocking long‑running use cases and reducing developer friction. This positions Ably as a critical piece of emerging AI infrastructure.

Key Takeaways

• HTTP request/response breaks for agents running hours
• Durable sessions add presence, state, and resumable streams
• Ably’s AI Transport shifts response path to mutable session layer
• Live objects keep human‑agent shared state in sync
• Plug‑ins for Vercel and TanStack enable drop‑in integration

Pulse Analysis

The rise of autonomous AI agents has exposed a fundamental mismatch between traditional web protocols and the needs of persistent, stateful computation. While HTTP excels at short, stateless calls, long‑running agents must survive network hiccups, tab switches, and user interruptions. Developers have begun coining the term "durable sessions" to describe a transport layer that preserves presence, shared state, and resumable streams—features essential for agents that reason over minutes or hours. Without such a layer, user experiences degrade, and enterprises risk abandoning high‑value AI workflows.

Ably leverages its real‑time messaging heritage to fill this gap with AI Transport, a two‑phase communication model. The client initiates a request over familiar HTTP, then the agent streams results back via a mutable session that supports token streaming and live objects. Mutable messages let reconnecting clients fetch the latest state instead of replaying the entire token history, while live objects synchronize collaborative data between humans and agents. By abstracting these capabilities behind plug‑ins for platforms like Vercel and TanStack, Ably offers a drop‑in upgrade that requires no architectural overhaul, letting developers focus on business logic rather than pub/sub intricacies.

The broader market implication is significant: reliable, multi‑device AI interactions become viable for sectors such as customer support, finance, and healthcare, where agents must operate continuously and maintain context. Ably’s approach reduces latency, improves resilience, and aligns with emerging standards for AI infrastructure, potentially setting a de‑facto baseline for durable session providers. As more frameworks embed similar abstractions, the industry will likely converge on a hybrid transport model, cementing real‑time platforms as indispensable backbones for next‑generation AI applications.