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the actual thing\nbeing judged is `orchestrator/orchestrator.py` and the transcript it produces.\n\n## Architecture\n\n```\nProblem Statement\n       │\n       ▼\n┌─────────────────────┐   spec.json (task list)   ┌──────────────────────┐\n│  Product Manager      │ ────────────────────────▶ │  Software Engineer   │\n│  Agent                 │                           │  Agent                │\n│  - breaks requirements │                           │  - implements each    │\n│    into buildable tasks│                           │    task as real code  │\n└─────────────────────┘                             └──────────────────────┘\n       ▲                                                       │\n       │            review verdicts (APPROVED / REVISE)        │\n       └───────────────────────────────────────────────────────┘\n                     (up to 2 review rounds, logged live)\n                                   │\n                                   ▼\n                     kanban-app/ (Flask + SQLite + React)\n```\n\n**Handoff contract:** the PM never hands the Engineer prose — it hands over a\nstructured `spec.json` (task id, component, description, acceptance criteria).\nThe Engineer never hands the PM prose either — it hands over `FILENAME:` +\na code block per task. The PM then reviews the actual code against the\nacceptance criteria it wrote, and can send tasks back for revision. This\nkeeps the collaboration structured and auditable instead of two chatbots\nfree-associating at each other.\n\n## Repository layout\n\n```\nREADME.md                          <- this file\norchestrator/\n  orchestrator.py                  <- the two-agent CrewAI system\n  requirements.txt\n  run_output/                      <- created when you run it\n    spec.json                      <- PM Agent's task list\n    AGENT_LOG.md                   <- full transcript: every prompt/response/verdict\n    generated_code/                <- code the Engineer Agent produced\nkanban-app/\n  backend/\n    app.py                         <- Flask + SQLite REST API (tested, working)\n    requirements.txt\n  frontend/\n    index.html                     <- React (CDN, no build step) Kanban board\n```\n\n`kanban-app/` is a hand-verified reference implementation so the board is\ndemo-able instantly, even before/without running the agents live. It mirrors\nexactly what `orchestrator.py` asks the Engineer Agent to build, so you can\nrun the live agent session during judging and show the parallel: \"here's\nwhat the agents just generated, and here's the working reference it matches.\"\n\n## How to run the agent system\n\n```bash\ncd orchestrator\npip install -r requirements.txt\nexport ANTHROPIC_API_KEY=your_key_here\npython orchestrator.py\n```\n\nThis will:\n1. Have the **PM Agent** turn the problem statement into `run_output/spec.json`\n   (6–9 concrete tasks covering backend + frontend).\n2. Have the **Engineer Agent** implement each task as real code, one task at a time.\n3. Have the **PM Agent** review every implementation against its own acceptance\n   criteria and return `APPROVED` or `REVISE` verdicts.\n4. Loop the Engineer back on anything marked `REVISE` (up to 2 rounds).\n5. Write final code to `run_output/generated_code/` and the entire\n   conversation — every prompt, every response, every verdict — to\n   `run_output/AGENT_LOG.md`.\n\n`AGENT_LOG.md` is the proof artifact: it's a timestamped record showing the\nPM specifying work, the Engineer building it, and the PM catching or approving\nit, with no manual coding by me in between.\n\n## How to run the Kanban board\n\n```bash\n# backend\ncd kanban-app/backend\npip install -r requirements.txt\npython app.py          # runs on http://localhost:5000\n\n# frontend — just open the file\nopen kanban-app/frontend/index.html\n```\n\nFeatures: three columns (To Do / In Progress / Done), create/edit/delete tasks,\ndrag-and-drop or button-based move between columns, empty-state messaging, and\na visible error banner if the API is unreachable. All persisted to SQLite via\na REST API (`GET/POST /api/tasks`, `PUT /api/tasks/:id`, `PATCH /api/tasks/:id/move`,\n`DELETE /api/tasks/:id`).\n\n## Demo narrative for judges\n\n1. Show `orchestrator.py` running live — narrate the PM writing the spec.\n2. Open `run_output/AGENT_LOG.md` and scroll through it as proof of a real\n   back-and-forth: spec → build → review → (optional) revision → approval.\n3. Open the working Kanban board and demonstrate it end-to-end.\n4. Make the point explicitly: *I supervised this; I did not write the app by hand.*\n\n## Design decisions worth defending\n\n- **CrewAI over a hand-rolled loop for the agent shells**, but the review/revision\n  control flow is explicit Python, not left to an autonomous \"crew\" black box —\n  this makes the collaboration auditable and demo-safe within a few hours.\n- **JSON as the interface between agents**, not free text, so a judge can see\n  a literal contract (`spec.json`) being handed from one agent to the other.\n- **A hand-verified reference app**, so a flaky LLM run during judging can't\n  sink the demo — the working Kanban board stands on its own regardless.\n","readmeExcerpt":"AI-Powered Multi-Agent Development System → Kanban Board **Author:** Poonam Singh **GitHub:** https://github.com/Poonam-Singh123 **LinkedIn:** https://linkedin.com/in/poonam-singh023 What this proves This submission is not \"a Kanban app.\" It's an orchestration system in which two CrewAI agents — a **Product Manager Agent** and a **Software Engineer Agent** — collaborate the way a real two-person dev team would, while","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"Problem Statement\n       │\n       ▼\n┌─────────────────────┐   spec.json (task list)   ┌──────────────────────┐\n│  Product Manager      │ ────────────────────────▶ │  Software Engineer   │\n│  Agent                 │                           │  Agent                │\n│  - breaks requirements │                           │  - implements each    │\n│    into buildable tasks│                           │    task as real code  │\n└─────────────────────┘                             └──────────────────────┘\n       ▲                                                       │\n       │            review verdicts (APPROVED / REVISE)        │\n       └───────────────────────────────────────────────────────┘\n                     (up to 2 review rounds, logged live)\n                                   │\n                                   ▼\n                     kanban-app/ (Flask + SQLite + React)"},{"language":"text","snippet":"README.md                          <- this file\norchestrator/\n  orchestrator.py                  <- the two-agent CrewAI system\n  requirements.txt\n  run_output/                      <- created when you run it\n    spec.json                      <- PM Agent's task list\n    AGENT_LOG.md                   <- full transcript: every prompt/response/verdict\n    generated_code/                <- code the Engineer Agent produced\nkanban-app/\n  backend/\n    app.py                         <- Flask + SQLite REST API (tested, working)\n    requirements.txt\n  frontend/\n    index.html                     <- React (CDN, no build step) Kanban board"},{"language":"bash","snippet":"cd orchestrator\npip install -r requirements.txt\nexport ANTHROPIC_API_KEY=your_key_here\npython orchestrator.py"},{"language":"bash","snippet":"# backend\ncd kanban-app/backend\npip install -r requirements.txt\npython app.py          # runs on http://localhost:5000\n\n# frontend — just open the file\nopen kanban-app/frontend/index.html"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"Two CrewAI agents — a Product Manager and a Software Engineer — collaborate through a spec → build → review loop to build a working Kanban board, fully logged and supervised. 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