Different agents infer different architecture.
Local reasoning becomes inconsistent system design.
Solutions
One attributed workspace for multi-agent development
Teams are starting to use more than one coding agent: one for planning, one for implementation, one for tests, one for docs. Windy gives all of them the same workspace — a shared knowledge base, proposals, task lists, and reviewed changesets — and a per-user MCP endpoint that attributes every change to a person, so every agent builds from the same source of truth.
The short version
Multi-agent development only works when the workspace is shared.
Multiple coding agents can help decompose, implement, test, and document software faster. But if every agent has a different view of the project, speed turns into divergence. Windy gives the project one workspace — a knowledge base, proposals, task lists, and reviewed changesets — that humans and agents can read, review, and update.
Windy is not another coding agent. It is the workspace that keeps many coding agents aligned.
The problem
More agents create more output — and more ways to drift.
A single coding agent can already go off-spec when design context is missing. With multiple agents, the risk compounds. One agent may infer an API shape, another may choose a different data owner, another may write tests against stale behavior, and a fourth may update docs from the wrong version of the plan.
Task order gets lost.
Work that should be sequenced becomes parallel in the wrong places.
Task lists disappear into one agent's chat.
Other agents cannot reliably continue from it.
Execution history fragments.
Nobody can quickly see what ran, what failed, and what still needs review.
Humans lose the review yardstick.
If agents do not share one design, there is no stable target to review against.
The bottleneck is not only agent capability. It is shared understanding.
Shared workspace
Give every agent the same project truth.
Multi-agent development needs a source of truth outside any one agent. The workspace should hold the design, constraints, task lists, and execution record that every agent uses before it acts. Humans should be able to inspect and change it through reviewed proposals and changesets. Agents should be able to read it and stage updates when the implementation changes.
- Specs and requirements — what the system should do.
- Architecture notes — how the system is shaped and why.
- Diagrams — workflows, data relationships, system maps, and cloud architecture.
- Contracts and schemas — APIs, events, database models, and payloads.
- Decision records — tradeoffs, constraints, and rejected alternatives.
- Task lists and tasks — ordered work with objectives, dependencies, prompts, and acceptance criteria.
- Execution history — what ran, what changed, what failed, and what still needs review.
The workspace should belong to the project, not to whichever agent touched the work last.
Windy model
Multi-agent work needs both a shared knowledge base and reviewable task lists.
A shared workspace is not just a wiki. Agents need to know what should be true, and they need an ordered path for changing it. Windy keeps the knowledge base and the work — proposals, task lists, executions, and changesets — in one place.
Knowledge base — the shared intent
Specs, architecture notes, diagrams, contracts, schemas, requirements, and decisions. Every agent can read the same intent before editing code, and knowledge-base edits land as reviewed changesets.
Work — proposals, task lists, executions
A proposal spec, then ordered tasks with objectives, dependencies, acceptance criteria, and prompts. Agents execute the work in a sequence humans can review, run after run.
The knowledge base tells every agent what should be true. The task list tells each agent what to do next.
Workflow
Decompose the work without losing the design.
A multi-agent workflow should not mean four agents improvising in parallel. It should mean one workspace, one task list, and clear task lanes.
01
Capture the design
Humans and agents create or refine specs, architecture, diagrams, contracts, and decisions in the knowledge base.
02
Create a task list
Large work becomes ordered tasks with objectives, dependencies, acceptance criteria, and agent-ready prompts.
03
Assign task lanes
One agent may plan, another may implement, another may write tests, and another may update the knowledge base.
04
Read before acting
Each agent reads the relevant knowledge base and current task before doing work.
05
Record execution
Each run leaves status, notes, and review context in the execution history.
06
Merge the changeset
When implementation changes the design, the update lands as a reviewed knowledge-base changeset.
The agents can divide the work because the workspace is not divided.
In practice
What this looks like in practice.
A team wants to launch audit logs across a SaaS product: data model, backend events, admin UI, filtering, export, tests, and documentation.
Without a shared workspace
One agent designs the event schema in chat. Another writes backend code from a different assumption. A third writes UI against a stale API. A fourth documents behavior that changed during implementation.
With Windy
- The audit-log spec defines events, actors, retention, filtering, export, and permissions.
- The architecture doc explains where events are emitted and stored.
- The API contract defines event payloads and query behavior.
- The task list breaks work into schema, event capture, API, UI, tests, docs, and rollout tasks.
- Each agent reads the same knowledge base and its assigned task before acting.
- Execution history records which tasks succeeded, failed, or need review.
- Knowledge-base docs and decisions are updated as reviewed changesets as the implementation evolves.
Multi-agent development becomes a coordinated plan, not a pile of parallel guesses.
Execution
Every agent run should leave a trail.
When multiple agents work on a project, the team needs to know what happened: which task ran, which files changed, which assumptions were made, which tests passed, and what still needs human review. Execution history turns agent activity into a durable project record.
- Task status — pending, running, blocked, succeeded, failed, or review needed.
- Agent notes — what the agent attempted and why.
- Review notes — what humans accepted, changed, or rejected.
- Follow-up tasks — what should happen next.
- Doc updates — what source-of-truth artifacts changed after the run.
Without execution history, every agent run becomes another disappearing conversation.
Control
Multi-agent does not mean human-out-of-the-loop.
Windy keeps the human as architect and reviewer. Agents can help write docs, decompose task lists, execute tasks, and record progress, but the workspace remains visible and reviewable, and every change is attributed to a person. That makes multi-agent work easier to steer instead of harder to understand.
- Per-(user, project) endpoints — each agent runs on a per-user MCP endpoint, so every change is attributed to a person.
- Reviewed proposals & changesets — humans can inspect and approve the design and edits agents stage.
- Ordered tasks — large work is decomposed into steps with dependencies.
- Acceptance criteria — every task has a clear definition of done.
- Execution history — agent runs leave a durable record.
- Revocable access — teams can control which agents can reach the workspace.
A shared, attributed workspace gives teams more control over multi-agent work, not less.
Windy
Windy is the workspace for multi-agent development.
Windy gives humans and coding agents one shared workspace: a knowledge base for specs, architecture, diagrams, contracts, and decisions, plus proposals, task lists, executions, and changesets. Humans review and shape it in the web app. Agents read and stage changes over a per-user MCP endpoint, so every change is attributed.
Your agents still write the code. Windy makes sure they build from the same workspace.
Best fit
When Windy helps most with multi-agent development.
Large feature rollouts
Work that spans backend, frontend, tests, docs, and migration steps.
Architecture-sensitive changes
APIs, schemas, service boundaries, event flows, permissions, and data ownership.
Parallel agent workflows
Teams assigning planning, implementation, testing, and documentation to different agents.
Long-running migrations
Work that must be paused, resumed, and reviewed across many runs.
Multi-tool teams
Projects using Claude Code, Cursor, Codex, OpenCode, or future MCP-aware agents together.
Codebases where maintainability matters
Projects where speed without shared understanding would create long-term risk.
FAQ
Questions, answered.
Multi-agent development is a workflow where multiple AI agents help plan, implement, test, document, or review software work.
Without one shared workspace, each agent may work from a different understanding of the design, task list, or current project state — and no one can tell which agent made which change.
Not exactly. Orchestration coordinates who does what. A shared workspace gives every participant the same knowledge base, proposals, and reviewed changesets to coordinate around.
Windy task lists structure work into ordered tasks with objectives, dependencies, acceptance criteria, prompts, and execution logs. Agents can use those tasks as the basis for their work.
No. Those tools write or assist with code. Windy is the workspace that holds the knowledge base, proposals, task lists, and changesets they build from.
Every agent runs on a per-user MCP endpoint, so each change is attributed to a person. Proposals and knowledge-base changesets are reviewed before they merge, so the team always sees who changed what and why.
Related
Keep exploring.
Windy is the workspace that keeps many agents aligned.
Your agents still write the code. Windy makes sure they build from the same workspace — and that every change is attributed and reviewed.
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