Tag: Agents

The MCP maintainers published an updated roadmap.

Since March, MCP went stateless (no more session handshakes), turned Tasks into an official extension for long-running work, and shipped enterprise auth pieces like issuer validation and Client ID Metadata Documents.

Next up: one HTTP-native transport instead of separate stdio and HTTP paths, real agent identity (workload federation, token exchange), and progressive tool discovery for large catalogs. No firm dates yet.

MCP is leaving its single-session, request/response origins behind for something built for long-running, multi-agent systems.

A common way to build an AI agent is to treat it as a long-running process. A worker receives a request, enters an agent loop, calls models and tools, waits for results, and eventually returns an answer.

This works well until agents start doing real work.

An agent may spend twenty minutes researching a problem, wait ten minutes for a build, ask a user for approval, or come back hours later when an external job finishes. Keeping a worker alive for the whole run wastes resources and makes failures expensive. A deployment, crash, or machine restart can also destroy work that has already happened.

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An oversimplified equation: agent harness = ai agent − model.

Overview

An agent harness is everything in an AI agent besides the model — everything outside the LLM inference call. It’s a piece of software that provides an environment for the LLM to observe and take actions.

1. The agent harness has a system prompt that defines the LLM’s basic behavior and controls how it puts out tokens.
2. The agent harness includes a set of tools that the LLM can call.
3. The agent harness interacts with the LLM to get tokens back.
4. The agent harness interleaves LLM outputs and tool calls, typically called the agent loop.

The system prompt

Before the user’s message arrives, the harness sends the model a fixed set of instructions: the system prompt. It sets the model’s baseline behavior and constrains how it generates tokens.

Tools

The harness also defines a set of tools the model can invoke: named actions, each with a description of what it does and how to call it. The model doesn’t execute these actions itself — it only requests them.

The model call

The harness communicates with the model through a single interface: it sends a prompt and gets tokens back. That call is the only boundary between the harness and the model.

The agent loop

The harness inspects the returned tokens for a tool call. If one is present, it executes the tool, appends the result to the context, and calls the model again. If not, it returns the output as the final answer. This send-inspect-act cycle is the agent loop.

Further reading

• What is a Harness? — Earendil
• Agent Harness — Microsoft Learn, Agent Framework docs
• The Anatomy of an Agent Harness — Vivek Trivedy, LangChain

Andrew Ng’s wrote an AI Engineering Skills Map, which lists six things to learn: LLM foundations, grounding models with data, building agentic systems, evaluation-driven development, operating in production, and machine learning foundations.

As he broke it down, I think the most important skill is learning how to build reliable systems from LLM’s uncertain behavior.

You don’t know in advance what an LLM will output

This is the only only truth you need to take away in this post, if you can’t remember all.

You can’t design AI software the way you design normal software — plan it, build it, ship it — because you can’t plan around an output you haven’t seen yet.

The AI engineering stack is packed with jargons now: MCP, CLI tools, sandboxes, memory and context management, harness, loop, RAG, prompt, multi-agent, (sorry, I cannot name them all, too much). But the core of AI engineering is surprisingly simple:

Build something, observe what it does, evaluate whether that’s good enough, change the weakest part, and repeat.