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Codenotary Releases AgenticMon

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. 

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Codenotary Releases AgenticMon

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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.