#llm-orchestration

#llm-orchestration

Over the past few years, I've reviewed thousands of APIs across startups, enterprises and global platforms. Almost all shipped OpenAPI documents. On paper, they should be well-defined and interoperable. In practice, most fail when consumed predictably by AI systems. They were designed for human readers, not machines that need to reason, plan and safely execute actions. When APIs are ambiguous, inconsistent or structurally unreliable, AI systems struggle or fail outright.

What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.

AI agents built on large language models (LLMs) often look deceptively simple in demos. A clever prompt and a few tool integrations can produce impressive results, leading newer engineers to believe deployment will be straightforward. In practice, these agents frequently fail in production. Prompts that work in controlled environments break under real-world conditions such as noisy inputs, latency constraints, and user variability. When building AI agents, it may begin hallucinating tool calls, exceed acceptable response times, and rapidly increase API costs.

The new capabilities center on two integrated components: the Dynamo Planner Profiler and the SLO-based Dynamo Planner. These tools work together to solve the "rate matching" challenge in disaggregated serving. The teams use this term when they split inference workloads. They separate prefill operations, which process the input context, from decode operations that generate output tokens. These tasks run on different GPU pools. Without the right tools, teams spend a lot of time determining the optimal GPU allocation for these phases.

In a 2024 study by Apollo Research, scientists deployed GPT-4 as an autonomous stock trading agent. The AI managed investments and received communications from management. Then researchers applied pressure: poor company performance, desperate demands for better results, failed attempts at legitimate trades, and gloomy market forecasts. Into this environment, they introduced an insider trading tip - information the AI explicitly recognized as violating company policy.

OpenAI has released Open Responses, an open specification to standardize agentic AI workflows and reduce API fragmentation. Supported by partners like Hugging Face and Vercel and local inference providers, the spec introduces unified standards for agentic loops, reasoning visibility, and internal versus external tool execution. It aims to enable developers to easily switch between proprietary models and open-source models without rewriting integration code.