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Goals, constraints, architecture, risk, review, and approval.
Solutions
Agent-native development
Agent-native development is software development designed around humans and coding agents working from the same project memory. Humans shape the design. Agents help plan, implement, test, document, and update execution history. Windy keeps the source of truth in one place.
Works with your agents
Claude CodeCursorCodexOpenCodeMCP-aware coding agents
Agent-native development starts with shared memory, not a longer prompt.
Prompt-driven AI coding asks the agent to infer the project from the current instruction and whatever context is attached. Agent-native development gives humans and agents a durable source of truth: specs, architecture, diagrams, decisions, plans, tasks, and execution history that survive across sessions and tools.
Windy is not another coding agent. It is the shared memory layer that makes coding agents usable as part of a real development workflow.
The shift
The old workflow was prompt, patch, forget.
In prompt-driven AI coding, each run often starts cold. The developer explains the task, pastes context, gets a patch, reviews the result, and then much of the reasoning disappears into chat history. That can work for small edits, but it does not scale to architecture-sensitive work, multi-session tasks, or multiple agents.
| Workflow | Starts from | Produces | Main weakness |
|---|---|---|---|
| Prompt-driven coding | Current prompt and selected context | Patch or answer | Context disappears after the run |
| Agent-native development | Shared project memory and plan | Code, docs, execution history | Requires a maintained source of truth |
Agent-native development is not just using AI more often. It is changing where the project context lives.
Model
Design, plan, execute, review, remember.
Agent-native development turns coding agents into participants in a project loop. The agent does not start from a blank prompt. It starts from the current design and task. After the run, the project memory records what happened so the next run can continue from the new truth.
01
Design
Capture requirements, architecture, diagrams, contracts, constraints, and decisions.
02
Plan
Decompose large changes into ordered tasks with objectives, dependencies, prompts, and acceptance criteria.
03
Execute
Coding agents read the relevant memory and implement a focused task.
04
Review
Humans compare the result against the spec, architecture, and acceptance criteria.
05
Remember
Docs, plans, decisions, and execution history are updated when behavior changes.
The memory makes the workflow continuous instead of episodic.
Memory layer
The source of truth has to include both design and execution.
A repository is necessary, but it is not enough. Agent-native teams need a memory layer that explains what the system should do, how it is shaped, what work is planned, and what happened in previous agent runs.
- Requirements and specs — behavior, edge cases, non-goals, and acceptance criteria.
- Architecture notes — components, boundaries, constraints, dependencies, and ownership.
- Diagrams — workflows, system maps, ER diagrams, state machines, and cloud architecture.
- Contracts and schemas — APIs, events, database models, payloads, JSON, YAML, and interface definitions.
- Decision records — tradeoffs, rejected alternatives, and reasons behind current design choices.
- Plans and tasks — ordered work with objectives, dependencies, prompts, and review criteria.
- Execution history — what ran, what changed, what passed, what failed, and what still needs review.
The agent-native source of truth must answer both: what should be true, and what happened last time?
Roles
Agent-native does not mean developer-optional.
Coding agents can generate code, propose plans, update docs, and execute tasks, but humans still own product intent, architecture decisions, tradeoffs, review, and release judgment. Agent-native development works best when the human role becomes clearer, not smaller.
Agents execute focused work.
Planning assistance, implementation, tests, docs, refactors, and execution notes.
Shared memory preserves continuity.
The design and execution record survive across people, agents, branches, and sessions.
The developer becomes the architect of the agent workflow, not the person rewriting the same context every run.
What this looks like in practice.
A team wants to add organization-level audit logs to a SaaS app.
Prompt-driven workflow
The developer writes a long prompt, points the agent at a few files, gets a patch, then repeats context for the API, UI, docs, and tests. Important decisions live in chat history.
Agent-native workflow with Windy
- The audit-log requirement defines events, actors, permissions, retention, filtering, export, and acceptance criteria.
- The architecture note explains where events are emitted, stored, queried, and displayed.
- Diagrams show event flow, admin UI flow, and data relationships.
- The plan breaks work into schema, event capture, API, UI, tests, docs, and rollout tasks.
- A coding agent reads the current task and relevant docs before editing code.
- Human review checks the result against the shared source of truth.
- Execution history records what changed, what failed, and what remains.
- Docs and plans are updated when implementation changes the design.
The work is no longer a series of disconnected prompts. It is a project workflow agents can participate in.
Infrastructure
Coding agents need a memory layer between prompts and the repo.
Agent-native teams need more than a model and a repository. They need a way for agents to access project memory, understand the current plan, record execution, and keep the source of truth current.
- Coding agentsClaude Code, Cursor, Codex, OpenCode, and future tools.
- Agent interfaceMCP and other tool interfaces that let agents access context and actions.
- Shared memory layerspecs, architecture, diagrams, decisions, plans, tasks, and execution history.
- Repository and systemscode, tests, deployments, logs, tickets, and product infrastructure.
The memory layer is what lets agents work on the project instead of only on the prompt.
Windy
Windy is the shared memory layer for agent-native development.
Windy gives each project one source of truth for specs, architecture, diagrams, contracts, decisions, plans, tasks, and execution history. Humans review and shape that memory in the web app. Coding agents read and write it over project-scoped MCP.
Windy Docs — design memory.
Requirements, specs, architecture notes, diagrams, contracts, schemas, and decisions.
Windy Plans — execution memory.
Ordered tasks, objectives, dependencies, acceptance criteria, prompts, and execution logs.
Your agents still write the code. Windy keeps the project memory they build from.
Best-fit use cases
Architecture-sensitive feature work
permissions, billing, authentication, workflows, integrations, and state machines.
Large refactors and migrations
work that needs sequencing, dependencies, and acceptance criteria.
Multi-agent teams
projects using Claude Code, Cursor, Codex, OpenCode, or future MCP-aware agents together.
Spec-driven teams
teams that want coding agents to build from durable requirements instead of one-off prompts.
Long-running projects
work that spans multiple sessions, branches, reviews, and agents.
Teams that want docs to stay current
projects where future agents need to understand why the code is shaped the way it is.
FAQ
Questions, answered.
Agent-native development is a software workflow designed around humans and coding agents working from shared project memory: specs, architecture, plans, tasks, and execution history.
AI-assisted coding often happens inside one prompt or editor session. Agent-native development makes agents part of a continuous workflow with durable memory, plans, review, and writeback.
No. Humans still own product intent, architecture, risk, review, and release decisions. Agents help execute focused work from shared context.
Without shared memory, every agent starts from partial context and may infer a different version of the project.
Windy provides the shared memory layer: Docs for design memory and Plans for execution memory, exposed to coding agents over project-scoped MCP.
Yes. Windy is agent-agnostic project memory for Claude Code, Cursor, Codex, OpenCode, and future MCP-aware coding agents.
Related
Keep exploring.
Your agents still write the code. Windy keeps the project memory they build from.
Start with one project, connect your agent over MCP, and build from a durable source of truth instead of a one-off prompt.
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