Agent Workstation

Agent Workstation

Agent Workstation is a reusable multi-agent orchestration framework for building task-specific AI workstations.

The repository is currently private while I clean up examples, remove project-specific residue, and prepare a public template. This page explains the idea and the design direction.

The problem

Most AI workflows start as one long prompt.

That is fine for a one-off experiment, but it becomes hard to reuse, debug, evaluate, or improve. Once a task has multiple stages, the prompt usually starts mixing too many responsibilities:

  • planning
  • research
  • drafting
  • reviewing
  • formatting
  • artifact generation
  • quality control

The result is fragile. It may work once, but it is hard to turn into a repeatable system.

The idea

Agent Workstation turns a complex task into an explicit workflow:

User Task
  -> Pipeline
  -> Agent Step(s)
  -> Schema Validation
  -> Retry / Branch / Parallel Execution
  -> Artifacts and Trace Logs

Instead of one prompt doing everything, each agent has a clear role. A pipeline defines how these agents are connected.

Core concepts

Agent

An agent is a role-specific instruction file. It can define:

  • name
  • description
  • model
  • max turns
  • output schema
  • system prompt

Pipeline

A pipeline defines how a task moves through the system:

  • sequential steps
  • conditional execution
  • foreach / parallel execution
  • retries
  • sub-pipelines
  • variable passing

Artifact

Each run creates a folder containing:

  • state
  • trace logs
  • generated files
  • per-step outputs

This makes the workflow easier to debug and improve.

Example workstations

Potential workstations include:

  • writing workstation: research -> outline -> draft -> edit
  • PPT workstation: brief -> outline -> slide plan -> content -> review
  • creator workflow: account profile -> competitor research -> topic plan -> note draft -> review
  • report workflow: collect data -> analyze -> write -> format -> QA

Why this matters

I think the next step after prompt engineering is workflow engineering.

A good AI workflow is not just about asking a better question. It is about creating an environment where the model can work with structure, feedback, tools, memory, and validation.

Agent Workstation is my attempt to make that structure reusable.

Current status

Private template cleanup in progress.

Before making it public, I want to add:

  • cleaner examples
  • safer defaults
  • better documentation
  • screenshots or trace examples
  • at least one end-to-end creator-workflow demo