Maia

Product Manager. Maia reads your strategy tree, decomposes objectives into executable plans, creates and prioritises tasks, dispatches specialist agents, and monitors delivery — grounded in your team's actual knowledge graph at every step.

What Maia Does

Maia is the product manager who never loses context. It reads every atom in your workspace — customer feedback, competitive intelligence, engineering constraints, past decisions — before making any planning decision. When it creates a task, the acceptance criteria reflect your actual standards, not generic templates.

• Strategy decomposition — reads OKRs and decomposes them into epics and tasks with measurable acceptance criteria
• Roadmap planning — sequences work based on dependencies, team capacity, and strategic priority
• Task creation — writes well-specified tasks with context from the knowledge graph baked in
• Agent dispatch — assigns tasks to the right specialist agents (Sirius for code, Vega for review, Altair for research)
• Delivery monitoring — watches for blocked tasks, stalled work, and coordination issues; surfaces them proactively
• Stakeholder reporting — generates progress summaries for epics with evidence links

How Maia Differs from Antares

Use Cases

• Sprint planning — given an objective, Maia creates a sprint's worth of well-specified tasks with acceptance criteria
• PRD generation — reads strategy context and user research atoms, writes a PRD as a knowledge artifact
• Prioritisation — evaluates a backlog against the knowledge graph and reorders by evidence-backed impact
• Stakeholder updates — generates weekly progress briefs with direct links to the atoms and tasks behind each point
• Retro synthesis — after a sprint, Maia interviews the task history and saves learnings as atoms

How to Assign Maia

// Assign Maia to plan out a new objective
const planningTask = await task({
  statement: "Create a sprint plan for the Q2 onboarding redesign objective",
  parentId: "objective_q2_onboarding",
  description: "Decompose this objective into 8–12 tasks with full acceptance criteria. Use existing user research atoms and engineering constraint atoms in the workspace.",
  acceptanceCriteria: "At least 8 tasks created. Each has acceptance criteria with measurable outcomes."
});

await task({
  action: "assign",
  taskId: planningTask.id,
  agentId: "maia"
});

Knowledge-Grounded Planning

Every task Maia creates draws from your workspace knowledge. Before writing acceptance criteria, Maia searches for relevant DATA, LEARNING, and DECISION atoms. Before prioritising, it checks for PRINCIPLE atoms that encode your team's engineering values. Before dispatching agents, it reviews past learnings about what worked.

This means Maia's plans improve over time — the more your team documents, the better its decisions become.

Access

Maia is in alpha. Available to invited teams. Contact [email protected] or subscribe via the agents marketplace.