Team Workflow
How teams of 2–10 developers use Agents Machine for shared memory, coordinated agents, and team automation.
For development teams, Agents Machine becomes a shared AI brain — every team member benefits from collective knowledge, consistent code standards, and automated workflows.
Setting Up for Teams
Shared Memory = Shared Knowledge
When one developer stores an architecture decision, every team member's AI knows about it:
Store in memory: API authentication migrated from JWT to session tokens.
All new endpoints must use the requireAuth middleware from auth.plugin.ts.
Migration completed by @alex on 2025-03-01.
Category: architecture, Tags: auth, api, migrationNow when any team member asks their AI about authentication, it returns the latest decision — not outdated patterns.
Team Coding Standards
Store your team's standards once, enforce them everywhere:
Store in memory: Team coding standards:
1. All React components must be functional with TypeScript
2. API responses use ok()/fail() helpers from @shared/utils
3. Database queries go through repository layer, never direct Drizzle
4. All user input validated with Elysia t.Object() schemas
5. Errors use AppError classes from @core/errors
Category: coding-rules, Tags: team-standards, typescript, reactProject Isolation
Each project gets its own isolated memory, vault, and kanban. Team members working on different projects never see cross-contaminated context.
Daily Team Workflow
Automated Standup
Set up a pipeline that generates standup reports from actual task data:
Create a pipeline called "team-standup":
1. Trigger (schedule, cron: "0 9 * * 1-5")
2. Agent (manager) — "Generate standup report from kanban:
- What was completed yesterday
- What's in progress today
- Any blockers or dependencies"
3. Skill (slack-notify) — post to #standupPR Review Pipeline
Automate first-pass code reviews:
Create a pipeline called "pr-review":
1. Trigger (webhook, path: "/hooks/github-pr")
2. Memory Search — find relevant coding standards and architecture decisions
3. Agent (reviewer) — review against team standards + memory context
4. Agent (analyst) — analyze impact and regression risks
5. Merge — combine both reviews
6. HTTP Request — post review as GitHub PR commentKnowledge Sharing
When a developer solves a tricky problem, they store the solution:
Store in memory: PostgreSQL connection pooling issue resolved.
Root cause: Drizzle default pool size (10) too low for our workload.
Fix: Set pool.max=25 in drizzle.config.ts.
Symptom: "too many clients" errors under load.
Category: service-context, Tags: postgres, drizzle, connection-pool, debuggingNext time anyone on the team hits a similar issue, the AI surfaces this solution automatically.
Team Vault
Shared Secrets, Individual Access
Store team API keys in the encrypted vault:
set_secret("GITHUB_TOKEN", "ghp_...", category="api_key")
set_secret("SLACK_WEBHOOK", "https://hooks.slack.com/...", category="webhook")- All team members can use secrets through skills (injected at runtime)
- Secrets never appear in logs or AI responses
- Full audit trail shows who accessed what and when
Kanban for Coordination
Sprint Planning with AI
Spawn the manager agent to decompose this feature into tasks:
"Add real-time collaboration to the document editor"
Create a board called "collab-feature" and add the tasks with priorities.The manager agent:
- Queries memory for existing architecture
- Breaks the feature into concrete tasks
- Assigns priorities (P0–P3)
- Identifies dependencies between tasks
Task Handoffs
When a developer finishes a task, the AI updates memory automatically:
Mark task collab-03 as done.
Store in memory: Real-time cursor positions implemented using
Yjs CRDT with WebSocket transport. See src/collab/cursors.ts.
Category: service-context, Tags: collaboration, yjs, websocketThe next developer picking up a related task gets full context.
Scaling Patterns
New Team Member Onboarding
New developers get instant access to the entire team knowledge base:
Query memory for all architecture decisions.
Query memory for coding standards and anti-patterns.
Query memory for recent service context.Instead of reading months of Confluence docs, the AI provides personalized, relevant context.
Retrospectives
Spawn the manager agent to run a sprint retrospective:
- Summarize completed tasks and their outcomes
- Identify patterns in blockers
- Suggest process improvements based on sprint dataTeam Plan Benefits
| Feature | Free | Pro | Team |
|---|---|---|---|
| Projects | 1 | Unlimited | Unlimited |
| Users | 1 | 1 | 5 |
| Shared Memory | — | — | Yes |
| Team Vault | — | — | Yes |
| Admin Dashboard | — | — | Yes |
| Project Isolation | — | Yes | Yes |