What are sub-agents?
Workshop’s main agent can spin up helper agents to handle specific tasks inside the same workflow. These are called sub-agents. Each sub-agent runs in its own separate context window. It reads files, runs tools, and does its work without consuming the main agent’s context. When it’s done, only a short summary comes back to your main conversation. Sub-agents don’t all need to use the same model. They can use different models, from different providers — and in Workshop Desktop, they can even run in different environments, including local models on your device.
Three sub-agents researching in parallel, each with a live progress indicator.
Why this matters
You no longer need one model to do everything. Different parts of a workflow can be delegated to the models best suited for those parts.Better performance
Some models are better at certain types of tasks than others. By delegating subtasks to better-fit models, the overall workflow produces better results than any single model could.
Lower cost
Not every task needs a top-tier model. Simpler or narrower subtasks can be routed to more affordable models, reducing overall cost without sacrificing quality where it matters.
Faster workflows
Some tasks benefit more from speed than from maximum intelligence — reading large volumes of text, scanning files, repetitive processing. These can be delegated to faster models to keep things moving.
More usable context over time
Sub-agents work without consuming the main agent’s context in the same way. You can keep building in the same conversation without hitting limits as quickly.
Privacy and control
Workshop can combine cloud models with local models. That means you can keep your main workflow powered by a frontier cloud model, but route a sensitive task to a local model running on your device. This is especially useful when a task involves:- Private data or sensitive internal files
- Content you don’t want sent to an external inference provider
- Compliance requirements around data residency
Local model routing requires Workshop Desktop. Workshop Cloud uses cloud-hosted models only.
The real differentiator
It’s not just “multiple models.” Workshop lets you orchestrate multiple models, from multiple providers, across cloud and local environments, in the same workflow — frontier cloud models, open-source models, and local models working together in one product experience. Use the best AI for each part of the job, instead of forcing one model to do everything.How sub-agents work
As conversations grow longer, the agent’s performance gradually declines — it may lose track of earlier decisions, repeat work, or miss details it would have caught earlier. This happens because every AI conversation has a context window: the total amount of information the agent can work with at once. Everything counts — your messages, files it reads, tool outputs, its own responses. You can see the context window filling up as a percentage in the UI. As it fills, quality drops. Eventually, you’ll need to either start fresh or use/compact to summarize and continue.
Sub-agents solve this by working outside your main conversation — each one gets its own fresh context window, does its work, and returns only a short summary. That keeps your main conversation lean and focused.
Why sub-agents matter
Take on bigger projects
Without sub-agents, every file read and tool call fills your context window, limiting how complex a task can be. Sub-agents handle the heavy lifting in their own space, so your main conversation stays focused on the big picture and stays sharp for longer.
Get higher quality results
A sub-agent with a single clear objective — “review this diff for security issues” or “explore how the payment flow works” — dedicates its full attention to that one job, without being distracted by the rest of your conversation.
Run work in parallel
Up to 3 sub-agents run simultaneously, each potentially on a different model. A quick search uses a fast model; a thorough code review uses a more capable one. You get speed and specialization without managing any of it.
Built-in agents
Workshop includes six specialized agents, each with a focused toolset:| Agent | What it does | Default model | Why | Best for |
|---|---|---|---|---|
| explore | Searches code, reads files, traces call chains. Read-only. | Haiku 4.5 | Fast read-only searches don’t need a heavy model | ”Where is this defined?”, “How does auth work?”, pattern searches |
| plan | Analyzes codebase, produces structured implementation plans with steps, critical files, and risks. | Inherits yours | Benefits from the same model you’re reasoning with | Planning before coding — especially multi-step features |
| verification | Adversarial checker that reviews your changes for regressions, missing tests, and edge cases. Returns PASS/FAIL verdict. | Inherits yours | Should match the reasoning depth of your main workflow | Sanity check after implementation, before you move on |
| reviewer | Code review specialist. Hunts for bugs, security issues, correctness problems. Can run linters and tests. | GPT-5.4 | Strong at code review and reasoning about diffs | PR-style review of a diff or changed files |
| web-search | Searches the web for docs, best practices, API references, error solutions. | Haiku 4.5 | Speed matters more than depth for lookups | Looking up documentation, debugging error messages |
| frontend | Full-stack frontend dev. Can read, write, edit files and run dev servers/build tools. | GLM-5.1 | Optimized for frontend and long-horizon UI tasks | Building or fixing UI components, CSS, frontend code |
You can override the default model for any built-in agent mid-conversation — see Choosing models below.
How to trigger sub-agents
Tell Workshop to use sub-agents in your prompt. The key phrase is “use sub-agents” or “delegate” — Workshop will recognize the intent and spin up the right agents:
All three research agents finished in 73.9 seconds. The main agent synthesizes the results and starts building.
Limits
- Sub-agents cannot spawn their own sub-agents (depth limit of 1)
- Each sub-agent starts fresh — it cannot see your conversation history, so include everything it needs in the delegation prompt
- Sub-agents complete autonomously — they cannot ask you questions mid-task
Choosing models for sub-agents
Each built-in agent has a default model chosen for its task type — shown in the Built-in agents table above. You can override any of them mid-conversation by just asking.Changing a model mid-conversation
You don’t need to configure anything upfront. Just tell Workshop which model you want a sub-agent to use, and it will apply that for the delegation:| Model | Provider | Best for | Speed | Cost |
|---|---|---|---|---|
haiku | Anthropic | Fast, focused tasks — searches, file scans, checklists | Fastest | Low |
sonnet | Anthropic | Balanced — code review, refactoring, documentation | Fast | Medium |
opus | Anthropic | Complex reasoning — architecture, multi-file analysis | Slower | Highest |
gpt-5.4 | OpenAI | Backend coding, code review, general-purpose reasoning | Fast | Medium |
gpt-5.4-mini | OpenAI | Lighter tasks at lower cost | Fast | Low |
gemini-3.1-pro | Frontend dev, long-context analysis (1M token window) | Fast | Medium | |
glm-5.1 | Z.ai | Frontend dev, long-horizon tasks, cost-efficient coding | Fast | Lowest |
Create your own agents
Workshop’s six built-in agents handle most workflows out of the box — you don’t need to create custom agents to use sub-agents. This section is for teams who want to define their own reusable agents for specialized or repeated tasks.
Quick start
Create anagents/ directory in one of the supported paths and add a markdown file:
.agents/agents/. If you only use Workshop, .workshop/agents/ is the simplest choice.
Each file is one agent. The filename (minus .md) becomes the agent name.
- qa-reviewer.md
- Using it
Agent file format
Each agent file uses YAML frontmatter followed by markdown instructions:| Field | Required | Description |
|---|---|---|
description | Yes | What the agent does. Workshop uses this to match your requests to the right agent. Be specific. |
model | No | Model to use: haiku, sonnet, or opus. Defaults to your conversation’s model. Use haiku for fast, focused tasks. |
tools | No | List of tools the agent can use (e.g., Read, Bash, Grep, Edit). Defaults to all tools. |
maxTurns | No | Maximum number of turns before the agent stops. Useful for keeping agents focused. |
disallowedTools | No | Tools the agent cannot use, even if otherwise available. |
hidden | No | If true, the agent won’t appear in the agent list but can still be used by name. |
memory | No | Memory scope: user, project, or local. Controls where the agent stores learned context. |
The
description field is crucial — it’s how Workshop decides which agent to delegate to. Write it like a job title and responsibility summary, not a vague label.Where to put agents
- Project agents
- Personal agents
Place agents in
.workshop/agents/ at your project root. These are shared with your team via version control — great for project-specific workflows like deployment checks, code review standards, or domain-specific analysis.Cross-client compatibility
Workshop implements the Agent Skills open standard, which means your custom agents are portable across a growing ecosystem of AI development tools — including Claude Code, Cursor, GitHub Copilot, Gemini CLI, OpenAI Codex, Roo Code, and more. Workshop discovers agents from three directory paths in priority order:| Path | Purpose |
|---|---|
.workshop/agents/ | Workshop-native (recommended) |
.agents/agents/ | Cross-client standard path — works in any compatible tool |
.claude/agents/ | Claude Code compatibility |
Writing effective agents
The quality of your agent depends on three things: a precise description, the right model, and clear instructions.Descriptions that trigger reliably
Thedescription field is how Workshop decides which agent to delegate to. Front-load the key action and context:
- Weak descriptions
- Strong descriptions
Choosing the right model
Workshop supports models from multiple providers. Refer to the full model table above for the complete list with speed and cost guidance. When setting themodel field in your agent file:
- Use
haikufor fast, focused tasks (searches, file scans, checklists) - Use
sonnetfor balanced tasks (code review, refactoring, documentation) - Use
opusfor complex reasoning (architecture decisions, multi-file analysis) - For frontend-heavy agents,
glm-5.1orgemini-3.1-proare strong choices - For backend code review,
gpt-5.4excels
model, the agent inherits your conversation’s current model.
Structuring instructions
Write instructions like you’re briefing a capable colleague who has no context. Include:- Role — Who the agent is and what lens it uses
- Steps — Numbered sequence of what to do
- Constraints — What to avoid or what boundaries to respect
- Output format — How to structure the response
Agents vs skills
Workshop supports both agents and skills. They serve different purposes:| Agents | Skills | |
|---|---|---|
| What they are | Autonomous sub-conversations with their own model, tools, and context window | Instruction documents loaded into your current conversation |
| How they run | In a separate context — can’t see your chat history | Inline — become part of your conversation |
| Best for | Delegated tasks: “review this”, “check that”, “research X” | Persistent knowledge: conventions, patterns, reference material |
| File format | Single .md file with frontmatter | Directory with SKILL.md and optional supporting files |
| Location | .workshop/agents/, .agents/agents/, .claude/agents/ | .workshop/skills/, .agents/skills/, .claude/skills/ |
Example agents
Deployment checklist
Deployment checklist
An agent that runs pre-deployment safety checks with a structured PASS/FAIL verdict:
API documentation generator
API documentation generator
An agent that reads your code and generates API documentation:Sort endpoints alphabetically within each resource group.
Test writer
Test writer
An agent that generates tests for existing code:
Best practices
Keep agents focused
One agent, one job. An agent that “reviews code, writes tests, and deploys” will do all three poorly. Create separate agents for each concern.
Use haiku for speed
Most custom agents don’t need the most powerful model.
haiku is fast, cheap, and capable enough for grep-and-report tasks. Save sonnet and opus for complex reasoning.Limit turns
Set
maxTurns to prevent agents from spiraling. A focused agent should finish in 10-15 turns. If it needs more, the instructions probably aren’t specific enough.Restrict tools
Only give agents the tools they need. A read-only reviewer doesn’t need
Edit or Bash. Fewer tools means fewer ways to go wrong.Specify output format
Tell the agent exactly how to structure its response — tables, checklists, JSON. Without this, you’ll get inconsistent free-form text.
Version control your agents
Commit project agents to your repo. They’re documentation of your team’s workflows, reviewable in PRs, and automatically available to anyone who clones the project.