An agent isn’t a prompt.
It’s a role on your team.
Name it, choose the model, write the mandate, scope the tools. The same definition runs the same way on every task, in every project — no matter who kicks it off. Define a roster once; let it work in parallel.
your own
An agent is a reusable role — configured once, governed centrally, and run the same way by everyone on your team.
A clever prompt lives on one person’s laptop. Nobody else can reuse it, nobody can audit it, and the next run drifts the moment the model changes. Good results stay anecdotal — they never become infrastructure.
An agent is a named role: a model, a system prompt, a credential, a skill set, and a tool allow-list — versioned in one definition. Assign it to a workflow step and it executes identically every time, with a traceable record on every run.
Model, mandate, credentials, skills, tools — all in one place.
Click an agent to see how each role is configured. Definitions are reusable across spaces and workflows, so a configuration that works gets shared — not rebuilt from a blank page.
AI that compounds across your organization.
When a developer finds the right model, the right prompt, and the right tool configuration for code review — that knowledge shouldn’t live on their laptop. In Spaces it becomes a shared agent definition: available to every team, assignable to any workflow, with a traceable record on every run.
And it gets better over time. Tune the prompt against real execution data. Swap the model when something faster ships. Tighten tool access as you learn what works. Every team using that agent gets the improvement automatically.
The right agent picks up the right step.
Each workflow step names the agent that handles it. When a task reaches implementation, the Implementer claims it. When it reaches review, the Code Reviewer takes over. When it reaches deploy, a human signs off. You decide how much to automate.
Assign agents to steps. Tasks route automatically.
See every run in the thread.
When an agent executes a workflow step, Spaces records a structured sequence of events: tool calls, file changes, cost milestones, and completion. Not a casual chat log — an attributable trail tied to the agent definition and the task.
Full control over every agent.
Everything you set lives in one definition — visible, auditable, and reusable across your organization. Six controls turn a prompt into governed infrastructure.
system_promptSystem prompts
Define what the agent knows, how it behaves, and the standards it follows — consistent behavior across every task.
modelModel selection
Pick the model and provider per agent — a fast model for routine work, a frontier model for review and security. Whatever fits the role.
skillsSkill tags
Tag agents with skills — coding, review, security, testing. Workflow steps match against skills and route work automatically.
toolsTool allow-lists
Control which tools each agent can touch. Restrict file access, limit API calls, enforce sandbox boundaries.
scopeScope control
Org-wide agents available everywhere; Space-scoped agents restricted to specific projects. You set the blast radius.
metricsPerformance tracking
Tasks completed, cost per task, error rate, cycle time. See which agents deliver and which need tuning.
Route each role to the model that fits it.
Code review on Claude Sonnet 4.6, visual QA on Gemini 2.5 Pro, a self-hosted endpoint for sensitive work — pick the model per role. Credentials are scoped per agent, so you swap models without rewriting workflows.
Spaces
Managed execution with built-in cost tracking and credentials
Anthropic
Claude Sonnet 4.6 and the full Claude lineup
OpenAI
GPT-4o and newer reasoning models
Gemini 2.5 Pro and the Gemini family
Self-hosted
Llama, Mistral, or any OpenAI-compatible endpoint