Built for the Agentic AI Hackathon 2026 · Team BinaryAgents
OnboardAI is a fully autonomous, multi-agent system that replaces manual client onboarding with intelligent, LLM-driven workflow execution.
Traditional onboarding is manual, slow, and error-prone.
OnboardAI replaces this with an LLM that thinks, decides, and acts — without human intervention.
┌─────────────────────────────────────────────────────────┐
│ OnboardAI System │
│ │
│ Input (client data) │
│ │ │
│ ▼ │
│ ┌─────────────┐ PLAN ┌─────────────┐ │
│ │ 🧠 Oogway │ ────────► │ ⚡ Po │ │
│ │ (Planner) │ │ (Executor) │ │
│ │ Gemini LLM │ ◄──────── │ Tool Layer │ │
│ └─────────────┘ RESULT └─────────────┘ │
│ │ │ │
│ │ DECISION ▼ │
│ └───────────────► ┌─────────────┐ │
│ │ 🛡️ Viper │ │
│ │ (Validator)│ │
│ └─────────────┘ │
│ │ │
│ Continue / Retry / Abort │
│ │ │
│ ▼ │
│ outputs/report.md │
└─────────────────────────────────────────────────────────┘
PLAN → ACTION → RESULT → DECISION → LOOP
Unlike a fixed pipeline, every step is decided dynamically by the LLM based on current state.
Remove the LLM and the system loses all intelligence — that's what makes it truly agentic.
| Agent | Character | Role | Technology |
|---|---|---|---|
| Planner | 🧠 Oogway | Decides the next action | Gemini 2.5 Flash |
| Executor | ⚡ Po | Runs the tool for that action | Python functions |
| Validator | 🛡️ Viper | Checks output, handles edge cases | Rule-based logic |
| Tool | Simulates |
|---|---|
send_email |
Welcome email dispatch |
create_drive_folder |
Google Drive folder creation |
create_notion_page |
Notion onboarding dashboard |
create_airtable_record |
Airtable CRM record |
| Scenario | System Response |
|---|---|
| Missing email | Validator issues ABORT — onboarding halts safely |
| Duplicate client | Validator issues WARN — logged, loop continues |
| Tool failure | Validator issues RETRY — up to 2 retries per step |
| Invalid input | Tool returns error, Validator escalates |
Every step is fully transparent:
[PLAN] LLM Reasoning: Email not yet sent and client has valid address.
[PLAN] Next action selected: SEND_EMAIL
[ACTION] Executor dispatching tool: SEND_EMAIL
[RESULT] ✉️ Welcome email dispatched to TechGlobal <hello@techglobal.io>
[DECISION] Validator approved. → Step 'SEND_EMAIL' validated successfully.
submission_BinaryAgents/
├── app/
│ ├── main.py # Entry point
│ ├── agents/
│ │ ├── planner_agent.py # Oogway — Gemini LLM decisions
│ │ ├── executor_agent.py # Po — tool dispatch
│ │ └── validator_agent.py # Viper — result validation
│ ├── tools/
│ │ ├── email_tool.py
│ │ ├── drive_tool.py
│ │ ├── notion_tool.py
│ │ └── airtable_tool.py
│ ├── core/
│ │ ├── agent_loop.py # PLAN → ACTION → RESULT → DECISION
│ │ └── state_manager.py # Session state & history
│ └── utils/
│ ├── logger.py # Color-coded structured logs
│ └── report_generator.py # Markdown report writer
├── data/
│ └── sample_input.json # Test client data (incl. edge cases)
├── docs/
│ ├── architecture.md
│ └── agent_flow.md
├── outputs/ # Generated reports land here
├── requirements.txt
└── .env.example
git clone <your-repo-url>
cd <repo-name>
pip install -r submission_BinaryAgents/requirements.txtcp submission_BinaryAgents/.env.example submission_BinaryAgents/.env
# Edit .env and set your GOOGLE_API_KEYpython submission_BinaryAgents/app/main.py| Layer | Technology |
|---|---|
| Language | Python 3.10+ |
| LLM | Google Gemini 2.5 Flash (google-genai) |
| Config | python-dotenv |
| Output | Markdown (outputs/) |
| Architecture | Custom multi-agent loop |
| Criterion | How OnboardAI Addresses It |
|---|---|
| Autonomy (30%) | LLM decides every step — no hardcoded sequence |
| Reasoning (25%) | Planner returns explicit reasoning per decision |
| Reliability (25%) | Retry, Warn, Abort tiers + heuristic fallback |
| Efficiency (20%) | No sleep delays, minimal footprint, clean loop |
Team BinaryAgents — Agentic AI Hackathon 2026 🚀