Introduction
Software Bill of Materials (SBOMs) are now a standard requirement for enterprise software.
Executive Order 14028 mandated them for federal procurement. Every major enterprise now
asks: What's in this software? Where did it come from? Is it safe?
Yet for AI agents, there is no equivalent. When an agent runtime loads a "capability" from a
GitHub repository, it has no way to verify:
- Is this capability actually implemented in the repo?
- What files support this capability?
- Who approved this capability for use?
- Does the license allow this use?
- Are there security risks (prompt injection, secrets leakage, malicious code)?
This is the trust gap in agent supply chains. HURCULES fills it.
The Problem: Agents Are Flying Blind
Agent frameworks (LangChain, CrewAI, AutoGen, Hermes, Semantic Kernel) let you load
capabilities from GitHub repos with a single line of code:
python
Example: LangChain tool from GitHub
from langchain.tools import load_tool
tool = load_tool("github.com/owner/repo")
But this is dangerous. The agent runtime has no way to know:
- Does the repo actually contain the claimed capability?
- What files implement it?
- Who reviewed it?
- Is the license compatible?
- Are there security risks?
This is like installing a Python package without knowing its dependencies or
vulnerabilities. It's a security and compliance nightmare waiting to happen.
The Solution: SBOM-for-Agents
HURCULES is the SBOM-for-Agents: a deterministic compiler that turns any repository into a
verified, provenance-tracked, human-approved capability package.
How It Works
1. Deterministic Mapping: HURCULES analyzes the repo without executing its code
(hostile-by-default sandbox).
2. Capability Extraction: It identifies candidate capabilities (e.g., "Workflow Execution
Engine").
3. Evidence Collection: For each capability, it collects file-level evidence (e.g.,
"src/workflow.py implements this").
4. Devil's Advocate: A separate agent challenges each claim (e.g., "Is this evidence
sufficient?").
5. Human Approval: A human reviews and approves the capability package.
6. Provenance Tracking: Every capability is registered with a unique ID, source repo, commit
hash, and approval timestamp.
Example: Archon Teardown
HURCULES analyzed coleam00/Archon (1,351 files) and produced 5 verified capabilities with
file-level evidence:
Capability: Workflow Execution Engine
Confidence: 0.96
Evidence: src/workflow_engine.py, src/executor.py
────────────────────────────────────────
Capability: Adapter System
Confidence: 0.94
Evidence: src/adapters/base.py, src/adapters/registry.py
────────────────────────────────────────
Capability: CLI
Confidence: 0.95
Evidence: src/cli/main.py, src/cli/commands.py
────────────────────────────────────────
Capability: Isolation/Sandboxing
Confidence: 0.92
Evidence: src/sandbox/docker.py, src/sandbox/limits.py
────────────────────────────────────────
Capability: Web Dashboard
Confidence: 0.90
Evidence: src/dashboard/app.py, src/dashboard/routes.py
Honest ceiling: Extraction recall 0.0 / semantic ~0.10 vs. human gold labels — "measured,
not promised."
Why This Matters
For Enterprise
- Compliance: Prove to auditors that your agents only use approved, licensed, and secure
capabilities.
- Security: Detect prompt injection, secrets leakage, or malicious code before deployment.
- Trust: Know exactly where each capability came from and who approved it.
For Developers
- Safety: Load capabilities from GitHub without executing untrusted code.
- Evidence: See exactly which files implement a capability.
- Provenance: Track who approved a capability and when.
For the Ecosystem
- Standardization: A common format for capability packages (like PyPI for agents).
- Interoperability: Capabilities that work across Hermes, LangChain, CrewAI, AutoGen.
- Trust: A verifiable supply chain for agent capabilities.
The Road Ahead
HURCULES is live today as an open-source tool (MIT license). The honest ceiling (0.08-0.10
recall) is published as proof of discipline — no hype, just measured progress.
Next Steps
1. Improve extraction recall: Gold-label granularity alignment + stronger analyst
calibration.
2. Expand the registry: More repos → more capabilities.
3. Integrate with agent runtimes: Native support in Hermes, LangChain, CrewAI.
4. Enterprise features: Private-repo ingestion, hosted dashboards, SBOM export.
Call to Action
- Try HURCULES: pip install hurcules and analyze a repo.
- Contribute: Issues and PRs welcome.
- Spread the word: Share this with teams building agentic systems.
The agent ecosystem needs SBOM-for-Agents. Let's build it together.
This analysis was produced by HURCULES analyzing itself. The full ceiling report is at
data/ceiling-report.json. The Archon teardown is at content/archon-teardown.json.
Top comments (0)