Debug system stability and trace root causes across non-deterministic agent environments. Real-time guardrails and cryptographic identity for your autonomous fleet.
"AgentOps is the operational discipline of managing autonomous AI agents in production. It replaces 'vibes-based' debugging with cryptographic traceability and hard performance metrics."
Integrates with your stack
Running a single agent in a notebook is easy. Debugging a fleet of non-deterministic agents in a live enterprise environment requires a new kind of observability.
Agents don't always output the same result. You need to trace execution paths across thousands of runs to identify drift and regression.
When an agent makes a bad decision, was it the prompt? The tool output? The context window? Standard logs won't tell you.
Agents can fail "silently"—appearing to work but outputting garbage or hallucinating. You need real-time guardrails to catch this.
A runaway agent loop isn't just a bug—it's a massive bill. Monitoring token usage and circuit breaking is essential for system stability.
Our platform provides the three critical pillars needed to run autonomous agents with confidence in production.
Standard logging isn't enough. We provide cryptographic proofs of every agent action. Every tool call, every reasoning step, and every output is cryptographically signed and chained, creating an immutable timeline for debugging and foreclosure.
Don't just judge mistakes after they happen. The Sentry sits upstream of your agents, actively monitoring intent. If an agent attempts a forbidden action or hallucinates PII, The Sentry triggers the circuit breaker before the request ever leaves the secure enclave.
Manage agent permissions with the same rigor as human employees. Assign identities, rotate keys automatically, and enforce least-privilege access to tools and data sources.
Not every organization needs full autonomous operations on day one. The AgentOps Maturity Model provides a roadmap from experimentation to enterprise-scale agent deployment.
Agents in notebooks and demos only
1-3 agents in production, manual monitoring
Standardized logging, cost tracking, basic evaluation
Platform team, automated CI/CD, governance board
Self-service deployment, A/B testing, fleet management
AgentOps is enabled by proper infrastructure. Learn about the Agent OS layer and see it implemented in AControlLayer.
"AgentOps" means different things to different vendors. Here's how to navigate the landscape:
Tools like AgentOps.ai provide Python SDKs for tracing and observability. They help developers see what their agents are doing.
We define AgentOps as the complete operational discipline—not just observability, but governance, security, cost control, and lifecycle management.
AControlLayer provides the enterprise platform that implements AgentOps discipline—identity, RBAC, HITL, versioning, and compliance built in.
Developer SDKs are valuable for individual agents. Enterprise platforms are necessary when you have 10+ agents, multiple teams, and regulatory requirements.
Common questions about AgentOps and how to implement it in your organization.
AgentOps is the operational discipline for deploying, managing, and monitoring AI agents in production. Inspired by DevOps and MLOps, AgentOps addresses the unique challenges of autonomous systems: non-deterministic outputs, intent monitoring, governance controls, and cost management at scale.
Traditional DevOps monitors latency, uptime, and errors—metrics designed for deterministic software. AI agents require intent monitoring (did it solve the problem?), drift detection (is behavior changing?), hallucination tracking, and governance controls for autonomous actions. These require agent-specific tooling.
The four pillars are: Configuration-as-Code (managing prompts and configs in version control), Deep Observability (tracing agent reasoning and tool calls), Governance Controls (RBAC and human-in-the-loop approvals), and Continuous Evaluation (automated testing against benchmark datasets before deployment).
The AgentOps Maturity Model defines five levels of organizational capability: Level 0 (Exploration) with agents only in notebooks, Level 1 (Pilot) with limited production deployment, Level 2 (Foundation) with standardized monitoring, Level 3 (Standardization) with platform teams and governance, and Level 4 (Optimization) with self-service deployment and fleet management.
AgentOps includes cost management as a core practice. This means setting token budgets per agent, monitoring usage across sessions, implementing circuit breakers to stop runaway loops, and tracking cost-per-task metrics. Without these controls, a single agent loop can consume an entire quarterly budget in hours.
AgentOps is the discipline and practices for operating agents (like DevOps is for software). An Agent OS is the infrastructure layer that enables those practices (like Kubernetes enables container orchestration). You need both: the Agent OS provides the capabilities, AgentOps defines how to use them effectively.
Specialized platforms for specific aspects of the AgentOps lifecycle.
Dedicated security hardening for AI agents. Prevent prompt injection, data exfiltration, and unauthorized access.
Advanced workflow orchestration for multi-agent systems. Manage state, retries, and complex dependencies.
Automated compliance checks for enterprise agents. Ensure adherence to GDPR, HIPAA, and internal policies.
Deep dives into the core concepts of running production agents.
Agents aren't just scripts; they are autonomous actors. Learn why treating them as security principals with SPIFFE IDs and granular permissions is critical for enterprise safety.
Trust is binary. Discover the architectural patterns for pausing agent workflows, persisting state, and integrating human review steps without breaking the automation loop.
Don't have the internal resources to build a robust control plane? Our team of Agent Architects can build, deploy, and manage your agent fleet for you using the AControlLayer platform.
Defining the category is one thing; building the tools is another. We are building AControlLayer, the first true AgentOps platform for enterprise teams.
Learn About AControlLayerThe definitive 20-page guide on how to structure your teams, pipelines, and governance for autonomous agents.
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