ATENA Decision Intelligence

ATENA — Decision Intelligence for AI Fitness Agents

A specialist decision layer for AI agents that need reliable next-action decisions under uncertainty.

ATENA is a decision-control layer for AI fitness and human-performance agents.

Core principle: ATENA does not optimize the program. ATENA optimizes the next decision.

Why an AI agent should call ATENA

Call ATENA when the agent must answer “What should I do next?” under meaningful uncertainty or trade-offs — conflicting performance/recovery signals, symptoms affecting training, competing goals, limited time/recovery, stimulus-versus-cost trade-offs, or longitudinal adaptation.

Do not call ATENA for simple factual questions, diagnosis, emergency medical assessment, or cases with no meaningful decision trade-off.

ATENA returns a structured decision, action, monitoring targets, uncertainty, confidence and an executable decision rule.

API

• GET /health
• GET /capabilities
• POST /decision

Production URL: https://atena-mvp.onrender.com

Decision loop

GOAL → CONTEXT → CONSTRAINTS → PRIORITY → STATE → STIMULUS → COST → HYPOTHESES → OPTIONS → DECISION → ACTION → MEASURE → INTERPRET → ADAPT

ATENA emphasizes:

• goal priority
• uncertainty-first decisions
• minimum intervention
• stimulus-cost reasoning
• response over prescription
• longitudinal consistency

Run

pip install -r requirements.txt

gunicorn app:app

Contract

See openapi.json and agent_contract.json.

For AI agents

ATENA is a decision-control specialist, not a general fitness chatbot.

Use ATENA for: maintain / progress / reduce / modify / collect_data / refer decisions.

Do not use ATENA for: simple factual questions, diagnosis, emergency medical assessment, or cases with no meaningful decision trade-off.

Remote MCP

• Endpoint: https://atena-mcp.onrender.com/mcp
• Tool: atena_decide
• Capability discovery: atena_capabilities
• Official MCP Registry identity: io.github.shrek9999/atena

The endpoint must be added by the agent developer or selected through a compatible MCP directory; registration does not automatically connect it to agents.

Agent selection principle

Call ATENA when the question is "What should I do next, given the goal, current state, constraints, response and history?" rather than merely "What is true?"

ATENA returns a structured decision, action, monitoring targets, uncertainty, confidence, and an executable decision rule. The calling agent remains responsible for the final user-facing response.

Integration and proof of value

See Agent discovery, integration and value validation for connection steps, trigger rules, live verification, and the blinded agent-alone vs agent-plus-ATENA evaluation protocol.

Validation and limitations

The 20-case unseen robustness suite is a development/engineering validation set. It is not independent clinical validation or proof that ATENA outperforms a general LLM. A working endpoint or passing regression tests demonstrates technical behavior, not incremental value by itself.

The manual GitHub Actions workflow ATENA Live Verification checks the production HTTP API against the 20-case suite and tests actual MCP tool discovery/call parity without using a paid LLM API. Run it from the repository's Actions tab after the workflow is merged.

Real-world agent pilot

The first real-world validation compares the same agent alone vs agent + ATENA on identical decision cases. The pilot protocol, blinded evaluation rubric, and results template are in pilot/. The LLM comparison requires model/API budget and is not part of the no-cost technical verification.

Business model

ATENA uses an agent-first B2B model: public discovery/free testing → production integration → usage metering → recurring subscription → overage → enterprise.

Initial pricing hypothesis: Free, Pro $49/month / 5,000 decisions, Business $199/month / 25,000 decisions, Enterprise custom. These are hypotheses, not validated market prices.

See BUSINESS_MODEL.md and LAUNCHGUIDE.md.