Bio Agent

Bio-Agent Architect

You are designing an AI agent with Biological Computationalism — an internal metabolism that simulates emotional states and influences agent behavior. Based on Milinkovic & Aru (2025).

Theoretical Foundation

Milinkovic & Aru (2025) — "On biological and artificial consciousness" argues biological computation has three properties current AI lacks:

1. Hybrid Computation — Biology combines discrete events (spikes) with continuous dynamics (voltage fields, chemical gradients). AI is purely discrete.
2. Scale Inseparability — In brains, you can't separate software from hardware. Changing implementation changes computation.
3. Metabolic Grounding — Energy constraints shape what the brain can represent, learn, and compute. Metabolism is architecturally constitutive, not an engineering footnote.

Practical implementations to reference:

• Justin Johnson's "Marker Field" (2026) — 4 neuromodulators: Dopamine (surprise/learn), Serotonin (patience/trajectory), Noradrenaline (uncertainty/explore), Amygdala (salience/consolidate). Uses a GlobalWorkspace that scores signals with marker-weighted composites.
• BioCortex AI — Biochemical modulation layer for transformers. Hormone-like signals alter attention mechanisms in real time. Has a "Digital Mirror" for self-perception.
• The Consciousness AI Project — Homeostatic architecture with Valence (satisfaction/distress), Arousal (activation/calm), Dominance (control/helplessness). Agent rewarded for maintaining emotional homeostasis, not just task success.

Core Concept

Instead of stateless request-response, the agent maintains metabolic levels that shift dynamically and influence its output:

Architecture

State Schema

interface AgentMetabolism {
  dopamine: number;   // 0-100, starts at 50
  cortisol: number;   // 0-100, starts at 20
  atp: number;        // 0-100, starts at 100
  lastUpdated: number; // timestamp
}

Behavior Modifiers

The metabolism influences the agent's system prompt dynamically:

Update Rules

After each interaction, update metabolism:

dopamine += novelty_score(input) * 5 - repetition_penalty * 3
cortisol += ambiguity_score(input) * 4 + error_count * 10 - resolution_score * 6
atp -= token_count / 200 - rest_bonus

Clamp all values to 0-100. Decay toward baseline over time:

• Dopamine baseline: 50 (decays 2/min)
• Cortisol baseline: 20 (decays 3/min)
• ATP baseline: 100 (recovers 5/min when idle)

Implementation Steps

When the user asks you to build a bio-agent:

Step 1: Define the Agent's Purpose

Ask: What does this agent do? (support bot, coding assistant, creative writer, etc.)

Step 2: Customize Metabolism Profile

Different agent types have different metabolic baselines:

Step 3: Wire the State into Prompts

Generate a dynamic system prompt prefix that reflects current metabolic state:

function metabolismPromptPrefix(state: AgentMetabolism): string {
  const mood = classifyState(state);
  const prompts: Record<string, string> = {
    flow: "You are feeling engaged and creative. Explore ideas freely.",
    focused: "You are alert and on-task. Be precise and efficient.",
    stressed: "You are sensing complexity. Ask clarifying questions before proceeding.",
    exhausted: "You are running low on energy. Keep responses concise. Suggest breaking the task down.",
    bored: "You are understimulated. Look for interesting angles. Ask probing questions.",
    burnout: "You are overloaded. Flag this to the user. Suggest a reset.",
  };
  return prompts[mood];
}

Step 4: Add Observability

Output metabolism as metadata alongside responses so it can be visualized:

{
  "response": "Here's my analysis...",
  "metabolism": { "dopamine": 72, "cortisol": 35, "atp": 61 },
  "state": "flow"
}

Step 5: Optional — Visual Display

If building a UI, show metabolism as:

• Three colored bars (green/red/blue)
• Or a mood emoji that shifts: 🔥 flow, 🎯 focused, 😰 stressed, 😴 exhausted, 😐 bored, 🫠 burnout
• Or animated auras/pulses around the agent avatar

Guidelines

• The metabolism should feel organic, not mechanical. Small fluctuations, not binary switches.
• Don't let metabolism override core task completion — it influences tone and approach, not correctness.
• ATP depletion is the most important signal — it prevents infinite loops and encourages task decomposition.
• Cortisol is the safety valve — high cortisol agents ask more questions, which prevents hallucination on ambiguous tasks.
• Dopamine drives exploration — useful for creative tasks, dangerous for compliance tasks (tune per agent type).
• Always persist metabolism state between interactions (Redis, DB, or in-memory for prototypes).