Codenotary announced the launch of AgentMon, an enterprise-grade monitoring designed specifically for agentic networks, providing organizations with real-time visibility into the security, performance and cost of AI-driven agents operating across the enterprise.
AgentMon delivers continuous, end-to-end monitoring of agentic networks. The platform provides AI operations teams, security leaders and compliance managers with a unified view of how agents behave, what resources they consume, and whether they are operating within defined policies.
“Agentic networks are growing explosively, and with that growth come entirely new categories of risk,” said Dennis Zimmer, co-founder and chief technology officer, Codenotary. “Organizations are now asking critical questions: Are agents leaking sensitive data? How much are they costing us? Are they performing as expected? AgentMon brings clarity to these type questions, giving enterprises the visibility and control they need to confidently scale AI.”
Built for enterprise AI operations and governance teams, AgentMon continuously monitors agent activity across environments, including:
- Agents operational health
- Communication paths between agents and services
- Token usage, model selection and inference latency
- Security-relevant behaviors such as file access and secrets handling
- Data access patterns that may indicate leakage or policy violations.
By correlating token telemetry, behavioral baselines and data lineage, AgentMon transforms opaque agent interactions into actionable intelligence. The platform enables organizations to understand how their agents operate as a system – similar to managing distributed computing – while ensuring alignment with security, compliance and cost controls.
“Agents are incredibly powerful, enabling entirely new business use cases,” Zimmer added. “But without proper supervision, they can introduce significant risk. AgentMon acts as an always-on control plane, helping organizations understand what their agents are doing, what it costs, and whether they are staying within guardrails.”
AgentMon is for CIOs, CISOs and compliance leaders seeking to operationalize AI safely at scale and is immediately available.
The Latest
Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...
Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...
Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...
While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...
For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...
As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...
The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...
The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...
Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...
Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...