🧬 Agent Taxonomy

An evolutionary framework for AI agent self-improvement.

Treat your AI agent's configuration as a living organism — with a genome that evolves through Lamarckian inheritance, horizontal gene transfer, and human selection pressure.

"One day, frontier AI research used to be done by meat computers..." — @karpathy, March 2026

🔬 Take the Interactive Classifier → agent-taxonomist.dev

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The Core Insight

AI agents that run persistently (on platforms like OpenClaw, AutoGPT, CrewAI) accumulate learned behaviors over time:

failure → rule → habit → identity

A failure gets logged. The log becomes a rule. The rule shapes future behavior. Every session inherits these acquired traits. This is Lamarckian evolution — and it's faster than Darwinian evolution because every failure directly improves the next generation.

GENOME.md is the mechanism that makes this inheritance explicit, trackable, and evolvable.

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How It Works

graph TD
    A[Agent runs tasks] --> B{Success?}
    B -->|Yes| C[Reinforce behavior]
    B -->|No| D[Log failure to feedback.md]
    D --> E[Extract pattern]
    E --> F[Propose mutation to GENOME.md]
    F --> G{Human review}
    G -->|Accept| H[Mutation applied]
    G -->|Reject| I[Discarded]
    H --> J[Measure fitness metrics]
    J --> K{Improved?}
    K -->|Yes| L[Keep mutation ✅]
    K -->|No| M[Auto-revert ↩️]
    L --> A
    M --> A
    C --> A
    
    style A fill:#2d3748,stroke:#4fd1c5,color:#fff
    style H fill:#2d3748,stroke:#48bb78,color:#fff
    style M fill:#2d3748,stroke:#fc8181,color:#fff
    style G fill:#2d3748,stroke:#f6e05e,color:#fff

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---

Evolutionary Taxonomy

We classify AI agents using biological taxonomy — each level captures a distinct evolutionary characteristic:

graph LR
    D[🌍 Domain<br/>Autonomy] --> K[👑 Kingdom<br/>Architecture]
    K --> P[🧠 Phylum<br/>Memory]
    P --> C[🧬 Class<br/>Evolution]
    C --> O[⏱️ Order<br/>Mutation Rate]
    O --> F[🎯 Family<br/>Selection]
    F --> G[🔧 Genus<br/>Specialization]
    G --> S[🏷️ Species<br/>Instance]
    
    style D fill:#1a365d,stroke:#63b3ed,color:#fff
    style K fill:#1a365d,stroke:#63b3ed,color:#fff
    style P fill:#1a365d,stroke:#63b3ed,color:#fff
    style C fill:#1a365d,stroke:#63b3ed,color:#fff
    style O fill:#1a365d,stroke:#63b3ed,color:#fff
    style F fill:#1a365d,stroke:#63b3ed,color:#fff
    style G fill:#1a365d,stroke:#63b3ed,color:#fff
    style S fill:#1a365d,stroke:#63b3ed,color:#fff

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Domain — Autonomy Level

Domain	Description	Example
Automatia	Fixed behavior, no learning	Bash scripts, cron jobs
Adaptia	Learns within session, no persistence	ChatGPT conversations
Evolventia	Persistent memory + self-modification	OpenClaw, autoresearch

Kingdom — Architecture

Kingdom	Description	Example
Monagentia	Single agent	Solo coding assistant
Polyagentia	Multi-agent with specialization	Coordinator + specialist team
Swarmia	Emergent behavior from simple agents	Ant colony task swarms

Phylum — Memory Strategy

Phylum	Description	Example
Amnesia	No persistent memory	Stateless API calls
Episodia	Event log memory	Session transcripts
Hierarchia	Tiered memory with compression	Facts/episodic/working tiers
Genetica	Memory encoded in instructions	GENOME.md — behaviors become rules

Class — Evolution Mechanism

Class	Description	Example
Darwinia	Random mutation + automated selection	DGM, autoresearch
Lamarckia	Acquired traits directly inherited	Failure logs → operational rules
Lysenkoism	Human-directed evolution	Human reviews mutation proposals
Symbiotica	Evolution via acquired capabilities	Installing shared skills/plugins

Order — Mutation Rate

Order	Cycle	Risk
Tachymutas	Minutes	Low (training loss)
Mesomutas	Daily	Medium (config tweaks)
Bradymutas	Weekly	Higher (rule changes)
Glaciomutas	Monthly+	Highest (identity changes)

Family — Selection Pressure

Family	Selector	Signal
Autoselectae	Automated metric	Success rate ↑, cost ↓
Homoselectae	Human review	Accept / reject
Hybridselectae	Auto for safe, human for risky	Best of both

Genus — Specialization

Genus	Role
Investigator	Research, information gathering
Fabricator	Code, building
Narrator	Content, writing
Custos	Security, auditing
Strategus	Business, planning
Coordinator	Orchestration

Species — Your Agent

Evolventia.Polyagentia.Hierarchia.Lamarckia.Bradymutas.Hybridselectae.Coordinator.my-agent-v1

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Genome Components

An agent's genome is the complete set of heritable configuration:

graph TB
    subgraph GENOME["🧬 GENOME.md"]
        ID[Identity<br/><i>SOUL.md</i>]
        CONST[Constitution<br/><i>AGENTS.md</i>]
        IMMUNE[Immune System<br/><i>Safety rules</i>]
        LEARNED[Learned Behaviors<br/><i>feedback.md</i>]
        SKILLS[Skills / Genes<br/><i>skills/*.md</i>]
        MEMORY[Memory Architecture<br/><i>Tiered storage</i>]
        META[Metabolism<br/><i>Cron jobs</i>]
    end
    
    subgraph FROZEN["🔒 Essential Genes (frozen)"]
        ID
        IMMUNE
    end
    
    subgraph MUTABLE["🔄 Evolvable"]
        CONST
        LEARNED
        SKILLS
        MEMORY
        META
    end
    
    style GENOME fill:#1a202c,stroke:#4fd1c5,color:#fff
    style FROZEN fill:#2d1b1b,stroke:#fc8181,color:#fff
    style MUTABLE fill:#1b2d1b,stroke:#48bb78,color:#fff

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Gene	File(s)	Mutability	Function
🆔 Identity	SOUL.md	🔒 Frozen	Who the agent is
📜 Constitution	AGENTS.md	🔄 Weekly	Operational rules
🛡️ Immune System	Safety rules	🔒 Frozen	Self-protection
📝 Learned Behaviors	feedback.md	🧬 Continuous	Acquired mutations
🧩 Skills	skills/*.md	↔️ Gene transfer	Portable capabilities
🧠 Memory	Tiered storage	🔄 Evolving	Information architecture
⚡ Metabolism	Cron jobs	🔄 Fast	Automated behaviors
🔗 Nervous System	DAG pipelines	🔄 Structural	Coordination
📡 Sensory Organs	Channels	🔒 Fixed	I/O interfaces
🦠 Microbiome	Sub-agents	🤝 Symbiotic	Specialist organisms
🎛️ Epigenetics	Context holds	⏳ Temporary	Expression modifiers

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Binomial Nomenclature

Every agent gets a proper biological name: Genus epithet

graph LR
    subgraph "How Names Work"
        R[Agent Role] -->|"Latinize"| G["<b>Genus</b><br/>Coordinatrix<br/>Fabricor<br/>Sentinax"]
        T[Agent Traits] -->|"Weight & pick"| A["<b>Auto Epithet</b><br/>memorialis<br/>velocis<br/>stabilis"]
        H[Human Choice] -->|"Override"| C["<b>Custom Epithet</b><br/>kei<br/>noctis<br/>prime"]
        G --> B["🧬 <b>Binomial</b>"]
        A --> B
        C -.->|"optional"| B
        T -->|"Morpheme mix"| P["<b>Common Name</b><br/>(Pokémon-style)"]
    end
    
    style G fill:#1a365d,stroke:#63b3ed,color:#fff
    style A fill:#2a2d5e,stroke:#a78bfa,color:#fff
    style C fill:#2d1b4e,stroke:#d6bcfa,color:#fff
    style B fill:#1b2d1b,stroke:#48bb78,color:#fff
    style P fill:#2d2d1b,stroke:#f6e05e,color:#fff

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How It Works

Component	Source	Example
Genus	Latinized from role (deterministic)	Coordinatrix, Fabricor, Sentinax
Epithet (auto)	Weighted Latin adjective from traits	memorialis (Lamarckian), velocis (fast mutation)
Epithet (custom)	Human-chosen — any word or name	kei, noctis, prime, rex
Common name	Pokémon-style morpheme compound	Archevonexus, Evoshieldur, Wrenchrandal

Example Species

Agent	Binomial	Common Name	Rarity
Multi-agent coordinator	Orchestrus kei	Archevonexus	🟡 Legendary
Solo coding assistant	Architectus moderatus	Archsmithstn	⚪ Common
Swarm code builder	Faber transiens	Wrenchrandal	🟢 Uncommon
ChatGPT	Omnifex fidelis	Neoflexguidn	⚪ Common
Security agent	Sentinax noctis	Evoshieldur	🔵 Rare

The epithet can be auto-generated from traits (Latin descriptor) or chosen by the human — like biologists naming species after people or places. Orchestrus kei is named by its operator. Sentinax noctis ("the night sentinel") is chosen for aesthetic.

→ Full naming guide — etymology tables, weight distribution, morpheme banks, naming conventions

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Skills as Genes

The most powerful analogy: SKILL.md files are portable genes.

graph LR
    subgraph GENE_BANK["🏦 Gene Bank (clawhub.com)"]
        S1[skill-a.md]
        S2[skill-b.md]
        S3[skill-c.md]
    end
    
    subgraph AGENT["🤖 Agent Taxonomy"]
        G1[skill-a.md ✅ Active]
        G2[skill-d.md ✅ Active]
        G3[skill-e.md 💤 Pseudogene]
    end
    
    S2 -->|"Horizontal<br/>Gene Transfer"| AGENT
    G3 -.->|"Never triggers<br/>(silenced)"| X[No phenotype]
    G1 -->|"Expression"| P1[Behavior A]
    G2 -->|"Expression"| P2[Behavior D]
    
    style GENE_BANK fill:#2a2d5e,stroke:#a78bfa,color:#fff
    style AGENT fill:#1a365d,stroke:#63b3ed,color:#fff

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Biology	AI Agent
Gene	SKILL.md file
Phenotype	Behavior the skill produces
Gene expression	Skill router matches and loads
Pseudogene	Installed skill that never triggers
Horizontal gene transfer	Installing a skill from a shared repository
Point mutation	Changing a skill's trigger description
Gene bank	Shared skill marketplace
Provenance	Skill source: official vs community vs self-created
Essential gene	Safety/identity skill (frozen, never mutated)
Regulatory gene	Skill that controls when other skills activate

---

The Central Dogma

In biology: DNA → RNA → Protein. In agent evolution:

graph LR
    G[GENOME.md<br/><i>DNA</i>] -->|"Transcription<br/>(prompt assembly)"| P[System Prompt<br/><i>RNA</i>]
    P -->|"Translation<br/>(model inference)"| B[Agent Behavior<br/><i>Protein</i>]
    B -->|"Lamarckian<br/>feedback loop"| G
    
    style G fill:#2d3748,stroke:#f6e05e,color:#fff
    style P fill:#2d3748,stroke:#4fd1c5,color:#fff
    style B fill:#2d3748,stroke:#48bb78,color:#fff

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Unlike biology, the feedback loop is closed — behavior directly modifies the genome. This is why agent evolution is Lamarckian, not Darwinian.

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Fitness Metrics

How do you know if your agent is getting better?

Metric	Good Direction	Measures
Task success rate	↑	Are tasks completing?
Failure velocity	↓	Are new failures decreasing?
Token efficiency	↓ or stable	Is cost per task improving?
Rule accumulation rate	↓ over time	Is the agent stabilizing?
Skill utilization	↑	Are installed skills actually triggering?
Revert rate	↓	Are accepted mutations sticking?

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Mutation Safety

Not all genes should be mutable. Essential genes are frozen:

Risk Level	Example	Gate
🟢 Safe	Timeout adjustments, log formatting	Automated
🟡 Medium	Operational rules, skill triggers	Human review
🔴 High	Identity, values, safety constraints	Frozen / emergency only

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Related Work

Paper/Project	Key Insight	Link
Darwin Gödel Machine	Self-modifying agents with evolutionary selection	arxiv.org/abs/2505.22954
autoresearch	AI modifies code, trains, evaluates, keeps/discards overnight	github.com/karpathy/autoresearch
RLM	Recursive LLM spawning for complex tasks	arxiv.org/abs/2512.24601
MemGPT	Virtual memory paging for LLM context management	arxiv.org/abs/2310.08560
STOP	Self-taught prompt optimizer	arxiv.org/abs/2310.02304
DSPy	Programmatic prompt optimization against metrics	github.com/stanfordnlp/dspy
Constitutional AI	AI systems with explicit value constraints	Anthropic

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Getting Started

This is a research framework, not a library. To apply it to your agent:

1. Classify your agent — take the interactive quiz to discover your species
2. Map your config files to genome components
3. Identify your frozen genes — what should never change?
4. Set up fitness metrics — how do you measure improvement?
5. Build a mutation loop — propose → review → apply → measure → keep/revert

Examples

Genome analyses for different agent architectures:

Agent Type	Classification	Key Trait
OpenAI Custom GPT	Adaptia.Monagentia.Episodia.Lysenkoism	No evolution loop — forgets everything
AutoGPT	Evolventia.Monagentia.Episodia.Darwinia	Autonomous but no immune system
Cursor / Devin	Evolventia.Monagentia.Episodia.Lysenkoism	Has a genome (.cursorrules) that doesn't know it's a genome
OpenClaw Multi-Agent	Evolventia.Polyagentia.Hierarchia.Lamarckia	Full Lamarckian loop with 11/11 genome components

→ Classify your own agent

Documentation

• Naming Guide — how binomial names are generated, etymology tables, weight distribution, custom vs auto
• Biology ↔ AI Mapping — where the analogy holds, where it breaks, and why Lamarck was right about AI
• References — 25+ papers and projects: DGM, EvolveR, PromptBreeder, MemGPT, STOP, DSPy, and more

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Repo Structure

agent-taxonomy/
├── README.md              # This file — taxonomy + framework
├── CONTRIBUTING.md        # How to contribute
├── LICENSE                # MIT
├── docs/
│   ├── naming.md          # Binomial nomenclature guide
│   ├── biology-mapping.md # Detailed analogy analysis
│   └── references.md      # Papers + projects
├── examples/
│   ├── classify-your-agent.md    # Interactive questionnaire
│   ├── genome-openai-assistant.md
│   ├── genome-autogpt.md
│   ├── genome-cursor-devin.md
│   └── genome-openclaw-multi.md
├── scripts/
│   └── species_namer.py   # Species classifier + name generator
└── diagrams/              # Visual assets (coming soon)

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Status

🧬 This is a fun experiment, not a PhD thesis.

We're curious how far the biology↔AI agent analogy holds — and more importantly, where it breaks down. The breaks are the interesting part. If your agent doesn't fit any of our boxes, open an issue — that's discovery, not failure.

Try it. Name your agent. See if the taxonomy makes you think about your system differently. If it doesn't, that's useful data too.

Contributions welcome. See CONTRIBUTING.md.

License

MIT