🤖Agent Harness: The Full Stack for AI Models
Building AI products is more than just models
TL;DR
The Agent Harness concept covers everything needed to turn an AI model into a production-ready product, from development tools to operations management. It's not just about the model, but the full stack around it.
The Agent Harness concept is about everything built around an AI model to make it a real product. It splits into two halves: Development and Operations. Development extends the model's capabilities, while Operations keeps the agent running smoothly. This isn't a new idea, but naming it makes it easier to design and manage. The model is the engine, and the harness is the chassis, brakes, dashboard, and seatbelts that make the product safe, reliable, and pleasant to use. A production agent needs to stay up, stay safe, keep costs in check, and provide visibility for troubleshooting. Building a good product takes more than just wrapping a model in an API.
Key Points
Agent Harness splits into Development and Operations halves, each with specific responsibilities.
Development includes memory across sessions, tools, retrieval, prompts, and orchestration.
Operations includes observability, evaluation, guardrails, routing, monitoring for drift and cost.
Two ways to build an Agent Harness: Harness-as-a-Service (HaaS) and self-managed.
HaaS provides managed APIs for configuration, while self-managed gives pieces to wire together.
Why It Matters
If you're building an AI product, the Agent Harness concept is crucial. It covers everything from development tools to operations management. For example, a small agent like FinBot uses the Agent Harness to summarize Q3 revenue by pulling filings from a document store, handing numbers to a code interpreter, and writing the summary. This approach ensures the product is reliable and pleasant to use.
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