rahulbalhotra-ship-it/kpmg_ai_team

1. Overall shape

- This repository is a workspace scaffold for a multi-component product, not a single app.

- It is organized around three runtime surfaces: a Python agent system, a backend service layer, and a browser-based frontend.

- Supporting folders provide shared contracts, database assets, docs, and deployment scaffolding.

2. Agent orchestration layer

- The agents/ package is the core AI/workflow domain.

- It contains individual role-based agents such as director, pm, design, dev, qa, consultant, and partner, suggesting a multi-agent collaboration model.

- agents/agents/graph.py, state.py, parser.py, generators.py, and llm.py indicate that this layer manages stateful agent execution, prompt parsing, output generation, and LLM interaction.

3. Agent workflows and tools

- agents/prompts/ stores prompt templates per agent role, which are part of the agent behavior boundary.

- agents/tools/ includes linter.py and test_runner.py, so agents can trigger or evaluate code-quality and test-related actions.

- agents/workflows/ and agents/graphs/ are placeholders for higher-level orchestration pipelines and graph definitions.

4. Backend service boundary

- The backend/ directory appears to be a separate Python service that exposes application functionality to the rest of the system.

- backend/app/main.py, agent.py, and logger.py suggest an application entrypoint, an agent-facing integration layer, and centralized logging.

- backend/src/api, services, db, middleware, and sockets indicate a conventional backend split into API handlers, business logic, data access, cross-cutting middleware, and real-time communication.

5. Backend and agent integration

- The presence of backend/langgraph/ strongly suggests the backend integrates with a graph-based agent orchestration runtime.

- This likely makes the backend the boundary between external clients and the internal agent system, translating requests into agent workflows and returning structured results.

- backend/uploads/ implies the backend also handles file intake or artifact storage related to workflows.

6. Frontend application surface

- The frontend/ folder contains a browser UI with index.html, game.js, assets.js, style.css, and a src/ tree.

- The src/components/chat, agents, and office folders suggest the UI is organized around conversation, agent visibility/control, and some kind of workspace/office metaphor.

- src/store, hooks, lib, and types indicate a client-side app with local state management and typed domain models.

7. Shared contracts

- shared/constants, shared/schemas, and shared/types are intended to hold cross-cutting definitions used by more than one layer.

- This is the key boundary for keeping the frontend, backend, and agents aligned on the same message shapes and domain concepts.

- For an architecture diagram, this should be shown as a shared contract layer connecting multiple runtimes.

8. Data and persistence

- database/migrations and database/seeds indicate a relational or schema-managed persistence layer, though the specific database engine is not shown here.

- backend/src/db is the backend-side access point to that persistence layer.

- The repo also has a top-level db/README.md, suggesting database setup or conventions are documented separately.

9. Infrastructure and local execution

- docker-compose.yml, infrastructure/docker, and infrastructure/k8s show support for both local container composition and Kubernetes deployment.

- run.bat indicates Windows-friendly startup support.

- .env.example suggests environment-driven configuration across components.

10. Documentation and planning artifacts

- docs/architecture/system_architecture.md is the primary architecture documentation source in the repo.

- docs/prd/ and deliverables/project_plan.md show product and delivery planning materials alongside implementation code.

- These are useful for understanding intended behavior, but the code layout itself already implies the runtime boundaries.

11. Testing strategy

- tests/agents, tests/backend, and tests/frontend show test separation by subsystem.

- test_flow.py suggests there is at least one higher-level end-to-end or workflow-oriented test that crosses boundaries.

- This reinforces that the system is meant to be validated both per layer and as an integrated flow.

12. Likely end-to-end flow

- A user interacts with the frontend.

- The frontend calls the backend over HTTP and possibly sockets for live updates.

- The backend invokes the agent orchestration layer to execute role-based workflows, possibly using graph/state logic and tools.

- Results are persisted through the backend’s DB layer and rendered back in the frontend.

13. How to draw the architecture

- Represent the system as three main boxes: Frontend, Backend, and Agents/Workflow Engine.

- Add Shared Schemas/Types as a cross-cutting layer between them.

- Add Database, Uploads, and External LLM/tooling as dependencies of the backend/agents side.

- Show Docker/K8s as deployment infrastructure rather than application logic.