In 2022, a typical AI application was a text box and a submit button. Over the past two years, three things collided: models got reliable enough to take actions, inference costs dropped low enough to run them in loops, and developers realized they could hand agents real tools. What followed wasn't gradual adoption. It was a land rush.
But the infrastructure never caught up. Teams built agent logic on top of tooling designed for single-turn chat: gateways that don't know what evals are, eval frameworks that don't talk to routers, observability tools that can't act on what they see. Every new capability means another vendor, another integration, another thing to break.
Today's developers are forced into a Frankenstein tooling stack, stitching together half a dozen vendors that were never designed to talk to each other. Every integration point is a potential failure mode. An eval tool detects a hallucination, but the gateway keeps routing traffic to the failing model anyway. Nobody told it to stop. By the time teams are done debugging fragmented traces and manually syncing data across tools, they've spent more time on plumbing than on the agent itself.
As agent workloads scale toward trillions of tokens, teams running five-vendor stacks are going to hit a wall, and the teams that already switched to unified platforms will be lapping them.
Respan’s Autonomous Agent Platform
Respan closes the loop by providing an "eval-aware" gateway that merges LM gateway functions, evaluations, observability, and prompt optimization into a single automated platform. Unlike traditional stacks, Respan makes agent logs the input for optimization, effectively creating a "self-driving" evaluation system with a native LM gateway.
The Respan lifecycle works in three stages:
Multi-environment & Controlled Deployment: The implementation begins in multi-environment workflows, where dev, staging, and production environments are decoupled to allow for rigorous stress-testing before agent goes production. Then the agent builders can use granular RBAC paired with load balancing, automated fallbacks and auto-retries to set up a resilient foundation, while a managed semantic caching layer protects the budget by eliminating redundant LLM costs.
Long-context & Agent Consumption First: Once in production, many long-context agents may hit 504 timeouts while waiting for an LLM to reason through 100k+ tokens. Respan eliminates this by handling edge cases like NGINX tuning and asynchronous logging, ensuring the stack never times out. As traces flow in, agent builders aren’t just looking at a static dashboard - they access a rich UI to slice and dice traces by any metadata, with every granular view backed by an agent-first API designed for agent consumption.
Eval-aware Gateway: When things go wrong in a fragmented stack, for example when an evaluation tool detects a hallucination but the gateway remains oblivious and continues routing traffic to a failing model, Respan closes this loop. Because the gateway is natively coupled with real-time evals, a hallucination trigger in production doesn’t just end up just in a log file - it immediately informs the LM router. This allows agent builders to trigger automated fallbacks to more reliable models or switch to “safe-mode” prompts instantly, optimizing for both reliability and cost.
Respan already routes over 80+ trillion tokens cumulatively in production across multi-environment deployments with granular RBAC, automated fallbacks, and a semantic caching layer that eliminates redundant inference costs. The edge cases that take down most stacks at scale, including long-context timeouts and async logging under load, are handled at the platform level. Retell AI, Mem0, Mercor, AlphaSense, Octolane AI, and Gumloop are all active customers. For mission-critical deployments, Respan backs the platform with dedicated 24/7 support.
When eval signals need to inform routing decisions in real time, a five-vendor stack isn't a tradeoff, it's a structural contradiction. The next generation of production agents won't be routed by price alone: they'll be routed by trace patterns, eval signals, and real-time reliability scores. That requires a control plane where the gateway, eval loop, and prompt optimizer are the same system.
If you're scaling agents, you should be talking to Respan.