Galileo Agent Control: Open Source Guardrails for Production AI
Despite tremendous enthusiasm and investment in AI agents, a sobering reality persists: most organizations can monitor what their agents are doing but cannot stop them when something goes wrong. This governance gap defines the production AI challenge, and on March 11, Galileo released an open source solution that addresses it directly.
Agent Control is an open source control plane that lets organizations define and enforce behavioral policies across all their AI agents. The project shipped under Apache License 2.0, backed by integrations with Strands Agents, CrewAI, Glean, and Cisco AI Defense. For AI engineers building production systems, this represents infrastructure that should have existed years ago.
| Aspect | What It Means |
|---|---|
| The Problem | Organizations can observe agents but cannot control them at runtime |
| The Solution | Centralized policy layer that blocks or steers agent behavior |
| License | Apache 2.0, vendor neutral, community supported |
| Integrations | Strands Agents, CrewAI, Glean, Cisco AI Defense on day one |
| Key Capability | Write policies once, enforce everywhere without downtime |
Why Runtime Governance Matters Now
According to IDC, use of AI agents among Global 2000 organizations is expected to increase tenfold by 2027. Token and API call volumes will spike by a factor of 1,000. Gartner predicts that by end of 2027, more than 40% of agentic AI projects will fail or be canceled due to escalating costs, unclear business value, or insufficient risk controls.
Through implementing AI agent systems at scale, I have observed a consistent pattern: teams that skip governance architecture regret it within months. An agent that works perfectly in development starts hallucinating in production when edge cases appear. By the time you notice, damage is done.
The practical implication is straightforward. Runtime governance cannot be an afterthought. It must be built into the agent architecture from day one, with clear policies that adapt without requiring code changes or downtime.
How Agent Control Works
Agent Control provides two primary control mechanisms: deny and steer.
Deny acts as a hard block. When a deny control matches, the AgentControlPlugin raises a ControlViolationError and execution stops immediately. Use this for content that must never proceed: credentials in tool arguments, SQL injection patterns in queries, or PII in model output.
Steer provides a corrective signal. Instead of stopping the agent, a steer control surfaces what the policy found and asks the model to try again with that guidance. This keeps workflows moving while maintaining boundaries.
The system connects to any agent framework through a lightweight SDK. Policies update in real time without requiring engineers to take agents offline. This matters enormously for production systems where downtime means lost revenue or degraded user experience.
According to Galileo’s CTO Yash Sheth: “With Agent Control, developers can now create policies in one place and then use those to enforce guardrails everywhere. We decided to make this open source so every agentic platform and every enterprise can leverage this critical infrastructure for all AI agents.”
Common Use Cases for Enterprise Deployment
Galileo anticipates several primary use cases that reflect real production challenges:
Preventing LLM Hallucinations: Policies can enforce citation requirements, cross-reference against known good data, or require confidence thresholds before allowing certain responses.
Enforcing Data Privacy Rules: Agents accessing customer data need guardrails that prevent PII from leaking into logs, responses, or external tool calls. Agent Control can block these patterns at runtime.
Steering Model Selection for Cost Control: When token costs spike, policies can route requests to smaller models for routine queries while reserving expensive models for complex reasoning tasks.
Ensuring Tone Consistency: Customer-facing agents need consistent voice and messaging. Steer controls can guide responses toward brand guidelines without blocking legitimate interactions.
These capabilities address the security concerns around AI agents that have kept many organizations from moving beyond pilot deployments.
The Bounded Autonomy Pattern
Perhaps the most valuable insight from production AI deployments in 2026 is the “bounded autonomy” pattern. Instead of building fully autonomous agents or keeping humans in every loop, successful teams give agents clear operational limits, mandatory escalation paths for high-stakes decisions, and comprehensive audit trails.
Agent Control fits this pattern precisely. Define what agents can do independently, what requires human approval, and what must never happen. The control plane enforces these boundaries without requiring constant human supervision.
Research from Gravitee’s State of AI Agent Security 2026 report found that 80.9% of technical teams have pushed past planning into active testing or production. Only 14.4% of those agents went live with full security and IT approval. This gap between deployment velocity and governance readiness creates substantial risk.
Why Open Source Matters Here
Runtime governance for AI agents could have become another expensive enterprise product. By releasing under Apache 2.0, Galileo made a strategic choice that benefits the entire ecosystem.
Vendor neutrality: Agent Control connects to any agent framework, not just Galileo’s commercial offerings. This reduces lock-in concerns that slow enterprise adoption.
Community contribution: Open development means the project can incorporate use cases and improvements from teams deploying agents across diverse industries.
Transparency: Organizations can audit exactly how governance works, crucial for compliance requirements in regulated industries.
The integrations with MCP-based agent frameworks and established platforms like CrewAI suggest the project will see rapid adoption among teams already building production agents.
Getting Started with Agent Control
The project ships with the control server, SDK, examples, and documentation on GitHub. For teams already deploying agents, integration follows a straightforward pattern:
First, install the Agent Control server and configure your policy definitions. Policies use a declarative format that specifies conditions, actions, and escalation paths.
Second, integrate the AgentControlPlugin into your agent framework. The SDK handles communication with the control plane, policy evaluation, and action enforcement.
Third, define your initial policy set based on your risk profile. Start with deny controls for clearly prohibited behaviors, then add steer controls for guidance on edge cases.
Warning: Agent Control is infrastructure, not magic. It enforces the policies you define. Poorly designed policies create either too much friction or insufficient protection. Invest time in policy design before deploying to production.
For teams exploring production agent architectures, Agent Control provides the governance layer that separates pilot projects from enterprise-ready systems.
The Governance Gap Will Close
The current state where organizations can observe but not control their AI agents is temporary. Between regulatory pressure, security incidents, and customer expectations, runtime governance will become table stakes for production AI.
Agent Control offers one path forward. Its open source nature, vendor neutrality, and practical feature set make it worth evaluating for any team deploying agents beyond development environments.
The broader trend is clear: AI agent development is maturing from “can we build it?” to “can we operate it responsibly at scale?” The teams that solve governance early will ship faster and with fewer incidents than those treating it as an afterthought.
Frequently Asked Questions
What agent frameworks does Agent Control support?
Agent Control integrates with any agent framework through its SDK. Launch partners include Strands Agents, CrewAI, Glean, and Cisco AI Defense, with community contributions expanding support continuously.
Does Agent Control require taking agents offline to update policies?
No. The system supports real-time policy updates without downtime. This runtime mitigation capability is essential for production environments where availability matters.
What is the difference between deny and steer controls?
Deny controls are hard blocks that stop execution immediately. Steer controls provide guidance that asks the model to retry with additional context, keeping workflows moving while maintaining boundaries.
How does Agent Control handle custom evaluators?
The platform accepts guardrail evaluators from any vendor as well as custom evaluators developed by enterprises. This flexibility allows teams to enforce organization-specific policies beyond generic content safety.
Recommended Reading
- AI Agent Development: A Practical Guide for Engineers
- AI Agents Are the New Insider Threat for Enterprises
- Agentic AI Foundation: What Every Developer Must Know
- AI Agent Evaluation and Measurement Frameworks
Sources
To see exactly how to implement AI agent systems in practice, watch the full video tutorial on YouTube.
If you’re interested in building production AI agents with proper governance, join the AI Engineering community where we share implementation patterns and best practices.
Inside the community, you’ll find discussions on agent architecture, governance strategies, and real-world deployment experiences from engineers shipping AI at scale.
