Kraken Networks builds AI governance, service mesh, and orchestration tools.
Open source, built for depth.
PRODUCTS
Python-first expert system runtime built on CLIPS. Deterministic reasoning with persistent working memory, compliance rule packs, and sub-millisecond latency.
engine = fathom.load("rules/governance.yaml")
result = engine.evaluate({
"agent": "analyst",
"action": "access",
"data_classification": "SECRET"
})
# => Decision(action="deny", reason="clearance insufficient")
Policy-first data broker for multi-agent systems. Three personas govern what agents know — the Librarian routes, the Curator learns, the Gatekeeper enforces.
response = nautilus.query(
intent="threat_analysis",
clearance="SECRET",
session="op-47"
)
# Librarian routes → Gatekeeper enforces → results attested
Full-stack platform for building and governing multi-agent systems. Go backend, React dashboard, DSPy reasoning modules, and CLIPS-based policy governors.
workflow.stage("analyze", {
module: "chain_of_thought",
agents: ["analyst"],
governor: "nist-800-53"
})
workflow.stage("review", {
require_approval: true,
on_failure: "escalate"
})
Agent action governance mesh. Controls what agents do — every action passes through Fathom-powered policy before execution.
bosun.authorize({
agent: "classifier",
action: "escalate",
context: ["session-47", "agent-a:output"]
})
# => Authorization(allowed=true, attestation="eyJ...")
20+ tools built / Herndon, VA
ARCHITECTURE
Kraken Networks builds a layered stack for AI agent governance. Each layer handles a distinct concern — reasoning, data governance, action governance, orchestration — so teams can adopt what they need without buying the whole platform.
At the base, Fathom provides deterministic reasoning using CLIPS, a battle-tested expert system runtime. Define governance policies in YAML, and Fathom enforces them with sub-millisecond latency and full audit trails.
Nautilus is the policy-first data broker. It mediates every data request through three personas — the Librarian routes, the Curator learns, the Gatekeeper enforces — tracking cumulative exposure across sessions so no agent quietly builds a dossier.
Bosun governs what agents do. Every action passes through policy before execution, with context declarations that create a complete audit trail of who did what and why.
Railyard orchestrates the fleet. A full-stack platform with workflow DAGs, DSPy reasoning modules, human approval gates, and real-time monitoring — giving you control over complex agent collaborations.
BLOG
Direct database access for AI agents is a liability. A data broker with three personas — Librarian, Curator, Gatekeeper — gives you deterministic control over what agents know.
Individual data requests look harmless. Accumulated across agents and sessions, they build profiles that shouldn't exist. Cumulative exposure tracking makes the invisible visible.
Policy engines evaluate rules. Expert systems reason about state. Here's why that distinction matters for AI agent governance.