Agentic Enterprise Runtime: Open Source Practice of Enterprise-Grade Multi-Agent Governance Runtime

Agentic Enterprise Runtime is an open-source governance runtime framework for enterprise-level AI agent orchestration, designed to solve governance challenges such as security coordination, permission control, and audit traceability in multi-agent scenarios. It corely supports tool policies, agent handover, tracking and auditing, red team testing, and manual approval workflows. The project is maintained by mohilamin and open-sourced on GitHub (link: https://github.com/mohilamin/agentic-enterprise-runtime), with a release date of 2026-06-03.

When large enterprises deploy specialized AI agents in multiple fields such as finance, fraud detection, and compliance, they face a series of infrastructure challenges:

• Agent tool access permission control
• Standardization of operation approval processes
• Arbitration of decision conflicts between agents
• Prevention of prompt injection attacks
• Cross-domain data access restrictions
• Full-link audit traceability of decisions These issues drive the demand for a governance runtime framework.

The project adopts a layered architecture, with core components including:

1. Agent and Tool Registry: 12 deterministic domain agents, 41 governed tools, and policy-based access control
2. Task Routing and Orchestration: 600 synthetic enterprise task routing logics, agent handover mechanism, conflict arbitration
3. Security and Governance: Red team testing scenarios (prompt injection, tool abuse, etc.), 8 probabilistic risk assessments, manual approval workflows
4. Observability and Auditing: Trace-based observability, decision lineage, executive-level reports and scorecards In terms of design philosophy, a deterministic runtime is used as the system record to ensure repeatable verification, security review, offline operation, and policy priority.

Technical implementation highlights:

• Deterministic Runtime: Local logic can be repeatedly verified without relying on LLM API, policy priority (agent suggestions are for reference only)
• Data and Persistence: DuckDB/SQLite provides data warehouse capabilities, synthetic data ensures safe and public operation
• Tech Stack: Backend Python+FastAPI, frontend Streamlit dashboard, testing Pytest (145 passed), code checking Ruff, containerization Docker+Docker Compose

The project has a complete validation system, with the latest validation results as of 2026-06-02:

• ✅ Pipeline tests passed
• ✅ Pytest (145 tests) passed
• ✅ Ruff code check passed
• ✅ Code quality document check passed Validation records are stored in docs/validation-log.md to ensure auditability and transparency.

The project is suitable for:

• Enterprise AI Infrastructure Teams: As a reference architecture for multi-agent governance runtime
• AI Security Researchers: Research on red team testing, policy execution, and audit tracking
• Compliance Audit Teams: Establishing auditable lineage and reports for AI decisions
• Recruitment Evaluation: Demonstrating AI infrastructure architecture capabilities (multi-agent orchestration, policy control, etc.)

Productionization path planning:

1. V0.2 Upgrade: Optional real-time agent adapter, trace-based observability, offline evaluation tools, red team testing scenario package, interactive approval workflow
2. Future Plans: Connect to real agent SDKs, integrate OPA/RBAC policy engine, export OpenTelemetry traces, integrate Jira/ServiceNow approvals, add key management and production security boundaries It is recommended that enterprise teams can refer to this architecture for prototype design and verification of multi-agent governance systems.