Different agents infer different architecture.
One agent may assume a service owns billing; another may put the logic somewhere else.
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Shared memory vs agent memory
Individual agent memory helps one assistant remember preferences or past interactions. Shared memory gives a team and its coding agents one project-scoped source of truth: specs, architecture, decisions, diagrams, plans, and execution history that everyone can read, review, and update.
Short answer
Agent memory belongs to one assistant. Shared memory belongs to the project.
Agent memory usually means information an individual AI assistant can carry across interactions: preferences, style, facts about a user, or past conversation context. Shared memory is different. It is a durable source of truth that multiple humans and agents can use together.
For software development, the most important memory is not what one agent privately remembers. It is what the project reliably remembers.
Agent memory
Individual memory helps with personalization and continuity.
Individual agent memory can be useful. It can remember how a user likes explanations, which tools they prefer, what naming style they use, or what was discussed in a previous session. That kind of memory helps an assistant feel more continuous and less repetitive.
- Remembering user preferences.
- Continuing a personal workflow.
- Avoiding repeated setup explanations.
- Adapting tone, style, or formatting.
- Recalling high-level facts that are safe and useful to reuse.
This kind of memory is helpful, but it is not enough to be the source of truth for a software project.
Shared memory
Shared memory is a source of truth that humans and agents use together.
Shared memory is durable, visible, and project-scoped. Instead of living inside one assistant, it lives with the project. Humans can review it. Coding agents can read it before implementation and update it when behavior 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.
- Decisions — tradeoffs, rejected alternatives, and constraints.
- Plans and tasks — ordered work with objectives and acceptance criteria.
- Execution history — what ran, what changed, and what still needs review.
Shared memory is not a private recollection. It is a project asset.
The problem
Software projects cannot depend on each agent remembering differently.
A software project is bigger than one assistant session. It has architecture, constraints, migrations, decisions, and multiple people changing it over time. If each coding agent has its own private memory, the project can drift into multiple versions of the truth.
Decisions become invisible.
A tradeoff remembered by one assistant may not be available to another agent or teammate.
Plans do not transfer.
A task sequence created in one chat is hard to resume in another tool.
Review gets harder.
Humans cannot easily audit hidden memory.
The project loses continuity.
Every tool has a partial, private view of the system.
Private memory may help the assistant. Shared memory helps the project.
Workflow
Every agent builds from the same source of truth.
Shared memory turns AI coding from isolated prompt sessions into a project workflow.
01
Capture the design
Store specs, architecture, diagrams, contracts, decisions, and constraints in one project memory.
02
Plan the work
Break large changes into ordered tasks with objectives, dependencies, and acceptance criteria.
03
Let agents read the same memory
Each coding agent starts from the project's current source of truth.
04
Review against memory
Humans compare implementation to the design and plan.
05
Update the shared memory
When behavior changes, the source of truth changes with it.
Shared memory makes context reusable across people, tools, and agent runs.
Comparison
Shared memory is not just another place to store text.
Chat history and repository docs both contain useful context, but neither automatically becomes shared project memory. The difference is whether the information is durable, reviewable, current, and usable by multiple agents.
| Context source | Good for | Weakness |
|---|---|---|
| Individual agent memory | Preferences and personal continuity | Private, opaque, and tied to one assistant |
| Chat history | Reconstructing a conversation | Hard to review, govern, and reuse across agents |
| Repository docs | Code-adjacent documentation | Often incomplete, stale, or not task-aware |
| Shared project memory | Human + agent coordination | Needs a deliberate workflow to keep it current |
Shared memory is defined less by where it is stored and more by how reliably humans and agents can use it as the source of truth.
Teams
Agent-native teams need memory that outlives any one tool.
Teams increasingly use multiple AI coding tools. A developer may use Claude Code for planning, Cursor for editor-local changes, Codex for task execution, and another agent for tests or documentation. Shared memory keeps those tools aligned around one project truth.
- A tech lead records an architecture decision once, and every agent can read it later.
- One agent creates a plan, another executes a task from the same plan.
- A teammate reviews the shared memory before approving a large change.
- Future agent runs inherit the updated design instead of starting from a stale prompt.
The memory should belong to the project because the work belongs to the project.
Windy
Windy is shared memory for coding agents.
Windy gives each project one shared 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 the same memory over project-scoped MCP.
Windy Docs — the shared design memory
Specs, architecture notes, diagrams, schemas, contracts, requirements, and decisions.
Windy Plans — the shared execution memory
Ordered tasks, objectives, dependencies, acceptance criteria, prompts, and execution logs.
Your coding agents still write the code. Windy gives them the same project memory to build from.
In practice
What shared memory looks like in practice.
A team is migrating from a simple billing model to seat-based billing with invoices, trials, and plan limits.
With individual agent memory only
One agent remembers a conversation about plan limits. Another agent knows the database schema. A third agent sees only the current prompt. The migration becomes a set of disconnected guesses.
With shared project memory
- The billing spec defines plans, seats, limits, invoices, trials, and edge cases.
- The architecture note explains which service owns subscription state.
- The diagram shows checkout, webhook, and invoice flows.
- The plan breaks the migration into ordered tasks.
- Every agent reads the same source of truth before working.
- Execution history records what changed and what remains.
- The shared memory is updated as implementation decisions evolve.
The team no longer depends on one assistant privately remembering the project. The project remembers itself.
Pitfalls
Common mistakes when thinking about shared memory.
Assuming private agent memory is enough.
It helps one assistant, but not a team of humans and agents.
Treating chat history as shared memory.
Chat is not automatically curated, current, or reviewable.
Letting each tool create its own truth.
Multiple memories create inconsistent agent behavior.
Making shared memory read-only.
If agents cannot update it when behavior changes, it still drifts.
Skipping human review.
Shared memory should keep humans in control, not hide project intent inside automation.
Saving everything.
Shared memory should be curated around durable design, constraints, plans, and execution facts.
FAQ
Questions, answered.
Agent memory usually belongs to one assistant or user. Shared memory belongs to the project and can be used by multiple humans and agents.
Not exactly. A wiki may be one input, but shared memory for coding agents should be structured, current, project-scoped, and accessible to agents during implementation.
Software work spans multiple people, tools, and sessions. Private memory can be hidden, stale, or unavailable to another agent.
Specs, architecture notes, diagrams, contracts, decisions, plans, tasks, acceptance criteria, and execution history.
In many coding workflows, yes. Agents should be able to propose or write updates when behavior changes, while humans retain review and control.
Windy stores project Docs and Plans, then exposes them to coding agents over project-scoped MCP so humans and agents build from the same source of truth.
Related
Keep exploring.
Give your coding agents the same project memory to build from.
Your coding agents still write the code. Windy gives them one project-scoped source of truth — specs, plans, and execution history — that humans and agents read together.
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