A governed delivery platform
for autonomous agents

Weaver plans before it builds, runs agents in parallel waves, and gives them production-grade context. Here is what that looks like, capability by capability.

Plan first. Then ship in parallel waves.

Weaver never lets an agent free-run on your codebase. Every task becomes a PHASER plan you approve. Weaver then dispatches parallel waves of agents and gathers the work back through review, test, and verify gates before anything merges.

  • A planner drafts the work. You approve at the plan-approval gate before a single line is written.
  • Independent steps fan out into parallel waves, so large features land in hours instead of days.
  • Deterministic dispatch with review, test, and verify gates keeps autonomy auditable.
  • Opinionated role and template presets mean you choose implementation, not scaffolding.
The PHASER orchestration flow An approved plan fans out into three parallel agent waves that converge through review, test, and verify gates before shipping. Plan ✓ approved Wave 1 agents in parallel Wave 2 agents in parallel Wave 3 agents in parallel Review Test Verify Ship
PHASER flow: an approved plan fans out into parallel agent waves that converge through review, test, and verify gates before shipping.

A delivery spine, not another IDE.

Work lives in an Idea, Domain, and Feature model with a real lifecycle: backlog, planning, in-progress, review, complete. It reads like Jira or GitHub Issues, so product and engineering share one governed queue instead of a chat log.

  • Ideas break down into Domains and Features with a shared, visible lifecycle.
  • Threads become an implementation detail beneath the PM model, not the interface.
  • A single triage queue surfaces plans awaiting approval, review, and follow-ups.
  • Every unit of work is traceable from intent to shipped change.

Feature lifecycle

  1. Backlog
  2. Planning
  3. In progress
  4. Review
  5. Complete
Idea · Checkout revamp Planning
Feature · Passkey login In review

Agents that see production, not just files.

Context that stays live. Weaver feeds agents live-system context (error tracking, logs, health, and dashboards) so they reason about the running system rather than a frozen snapshot of source. That is context an editor-bound assistant structurally cannot reach.

  • Wire in error tracking, logs, metrics, and dashboards as first-class context.
  • Agents cite real incidents and telemetry when they plan a fix.
  • Close the loop from a production signal to a reviewed, tested change.
  • Context the code editor never sees, because it lives beyond the repo.

Live-system context

streaming
  • Sentry TypeError · checkout.ts · 142 events
  • Logs p99 latency 1.8s on /api/plan
  • Health worker pool 3/4 · queue depth 12
  • Dashboards conversion down 4.2% since deploy

Agent context

The agent plans against the running system, not a stale snapshot.

Institutional memory your agents actually use.

A durable knowledge base and vector memory keep decisions, conventions, and domain docs where agents can retrieve them. New waves start with the context your team already earned instead of relitigating it every session.

  • Persist architecture decisions, conventions, and domain knowledge once.
  • Vector-indexed retrieval pulls the right context into each plan automatically.
  • Knowledge docs are co-editable, so the source of truth stays current.
  • Memory compounds: every shipped feature makes the next plan sharper.

Knowledge base

vector-indexed
  • Architecture decisions retrieved
  • API conventions retrieved
  • Domain glossary retrieved

Pulled into every plan automatically, with no re-explaining context.

A team surface, not a solo prompt box.

Presence, co-editing, comments, and shared PM chat make Weaver a place a team works together with its agents. Review a plan, leave a comment, and hand off. The whole org sees the same governed state in real time.

  • Live presence and co-editing across plans, knowledge, and the PM board.
  • Threaded comments turn plan review into a real conversation.
  • Shared chat keeps humans and agents in one auditable workspace.
  • Hand-offs are explicit, so nothing stalls waiting on one person.

Live on this plan

Three teammates are editing this plan together.

AR Ana R. on wave 2

"Approving the plan. Let's split the migration into its own wave so review stays small."

Managed multi-model execution, with keys optional.

By default Weaver runs your work across a managed mix of models, matching each task to the right one so quality lands where it counts and you never wire up providers yourself. Prefer to hold the keys? Bring your own provider credentials for extra control and flexibility.

  • Managed multi-model execution is the default: no provider setup, predictable operation.
  • Weaver routes each task to the right model tier, so quality lands where it matters.
  • Bring your own keys when you want direct control over providers and credentials.
  • Cost stays predictable and managed, with usage you can see and plan around.

Managed model routing

Fast Balanced Frontier Long-context
Bring your own keys Optional

See the whole platform in motion

Governed orchestration, a real PM spine, live-system context, and managed execution with predictable cost. See how Weaver measures up.