Structured Workflows for AI Agents: TypeScript, Python, and MCP

AI agents can complete tasks, generate code, and collaborate with other agents. But as workflows become more complex, a question becomes increasingly important:

How do we make work between AI agents structured, traceable, and easier to hand off?

Passing messages between agents is straightforward. Keeping track of requirements, decisions, results, validation, and unfinished work is much harder.

I've been working on an open-source project to explore a structured approach to this problem.

Imagine a research agent handing its findings to a coding agent.

The coding agent needs more than a summary. It may need the original requirements, constraints, decisions already made, results, unresolved questions, and the next steps.

Without a consistent format, every application ends up inventing its own conventions.

My project explores using Work Orders and Work Reports to represent work and its outcomes in a structured, reusable way.

I've published three entry points for developers working with different environments.

The core package provides the foundation for structured work management, including schemas, validation, lifecycle management, and work handoffs.

npm install agent-work-layer Enter fullscreen mode Exit fullscreen mode 2. MCP integration agent-work-layer-mcp

The MCP package makes supported work-management operations available through the Model Context Protocol, allowing compatible AI clients to interact with the work layer through MCP tools.

npm install agent-work-layer-mcp Enter fullscreen mode Exit fullscreen mode The project also explores an MCP gateway that can enforce work-order-specific tool permissions and record tool-call evidence.

Agent Work Layer — Python implementation

For Python developers, the project also includes a Python implementation.

This gives Python-based AI-agent applications a way to explore the same broader approach to structured work, without requiring the entire application to be written in TypeScript.

Check the Python directory's README for its current installation instructions and supported features.

The objective is to make agent handoffs less dependent on ad hoc prompts and more dependent on explicit, inspectable work structures.

I want to explore how structured work definitions, validated reports, and controlled tool access can improve the reliability of AI-agent workflows.

Rather than every project independently inventing its own work-order format, lifecycle rules, and handoff conventions, could these become reusable infrastructure?

If you're building AI agents, agent frameworks, or MCP integrations:

I'm interested in feedback, contributions, and real-world use cases from developers working on these problems.