Agentic AI’s proactive autonomy creates a new governance challenge. Observability is the key to keeping systems under control.
By: Dave Dimlich
President of SD3IT
The agentic AI horse is out of the barn. The important thing now is figuring out how organizations can track where it goes, what it does and what data it has access to across the entire enterprise.
Pilot programs are steadily, if very slowly, moving into production, employees are bringing shadow artificial intelligence into the network, and new platforms have put the creation of AI apps into the hands of non-technical business users as well as professional developers. All told, more than 80% of Fortune 500 companies now use AI agents built with low-code or no-code tools, according to Microsoft’s Cyber Pulse report, and those agents are becoming part of everyday operations across finance, cybersecurity, customer service, engineering and countless other business functions.
That growth marks a significant milestone for enterprise AI, but it also underscores the fact that many organizations may not be ready for it. They may not fully realize how radically agentic AI can change things within the enterprise. For decades, we’ve deployed software with the expectation that once it passed the testing phase and entered production, its behavior would remain largely predictable until the next update. AI doesn’t work that way. Models and infrastructures evolve, and the data they deal with can change. Agentic AI goes even further by making decisions, invoking tools, accessing enterprise systems and interacting with other AI agents.
Business and IT people know this, of course, understanding that deployment isn’t the finish line but the beginning of an ongoing operational responsibility. But the rate at which AI is developing and affecting the enterprise might still take some by surprise.
That’s why observability has become one of the most important discussions in enterprise AI. Organizations don’t just need confidence that an AI solution worked on launch day. They need continuous visibility into how it performs, who is using it, what data it’s accessing, what dependencies it’s creating, and whether it’s continuing to operate within the security, compliance and mission parameters the organization has established.
Governance Has to Keep Pace With AI
The old saying still applies: You can’t protect what you can’t see.
Unlike traditional software, which generally will behave tomorrow the same as it did yesterday unless someone changes the code, agentic AI introduces a different level of operational complexity. Autonomous agentic systems continuously interact with changing environments, connecting with databases, applications and other AI systems. There is no way of knowing ahead of time everything that an AI agent will do.
Changes wrought by AI can gradually affect accuracy, security or operational reliability without creating an obvious failure. An agent’s response to a request might become less reliable and, in some cases, mistakes can quickly cascade, being repeated if an AI system retrieves an incorrect bit of information from the output of other AI systems. An agent also might begin accessing information it shouldn’t. A workflow may start producing inconsistent results because of changes somewhere else in the technology stack.
That creates challenges that sound familiar to anyone who has managed shadow IT, except they can manifest themselves on a much larger scale. IT managers need the answers to questions such as:
- How many agents exist across your enterprise, and can you identify them?
- Who owns each one, and are they officially approved?
- What permissions granting access to systems and data have they inherited?
- What sensitive information can they access?
- Who is accountable for their operation?
- Are you able to quickly identify unusual behavior, declining performance or unexpected interactions between autonomous systems?
Without observability and governance, organizations can quickly lose visibility into systems that continue making decisions on their behalf.
Observability Is Infrastructure, not a Software Dashboard
AI observability may represent a new frontier for organizations, but achieving it can begin with security basics. As a starting point, for example, Microsoft argues that AI agents should be governed much like employees or service accounts, using established zero-trust principles such as least privilege, explicit verification and the assumption of compromise as a design principle. Those aren’t new security concepts. What’s changing is that AI, which must operate within those same enterprise controls, accelerates the way those controls are applied.
Beyond that, AI observability may require organizations to rethink how they run their enterprises.
One of the biggest misconceptions about AI observability is that it’s simply another monitoring tool. In reality, it’s an operational capability built across the infrastructure supporting AI. Organizations need centralized visibility across hybrid environments. The functions that can provide that visibility include:
- Logging that captures meaningful activity rather than overwhelming operators with data.
- Governance that connects identity, access management, policy enforcement and audit readiness.
- Lifecycle management that continuously validates AI performance as systems evolve.
Most importantly, they need these capabilities to be tightly integrated rather than scattered across disconnected products.
When in place, observability provides critical visibility by collecting operational signals that allow organizations to understand not only what an AI system is doing, but why it’s behaving that way. Instead of reacting to problems after users discover them, operational teams can detect changes early, investigate root causes and maintain confidence that an AI is operating as intended.
Getting to that point is where many AI deployments become difficult. The challenge isn’t collecting the telemetry. It’s turning thousands of operational signals into a unified picture and providing actionable information that technology leaders, security teams and mission owners can actually use.
How Governance Becomes an Operational Capability
This is where organizations could benefit by shifting their thinking. AI observability should become part of the organization’s broader operational architecture, rather than another isolated dashboard owned by one technical team.
At SD3IT, we help customers integrate the technologies that make that possible, providing organizations with clear visibility and operational resilience in complex, multi-domain environments. That begins with centralized visibility across distributed environments, including tactical edge deployments where operational awareness is often the most difficult to maintain. It includes secure infrastructure built around zero-trust principles along with centralized logging, anomaly detection and governance frameworks that help organizations understand how AI systems interact with users, applications and mission data.
Just as importantly, observability should span the entire lifecycle of an AI deployment. Logging, policy enforcement, identity management and operational monitoring should be designed into the architecture from the beginning. As AI capabilities evolve, organizations need the ability to adapt security policies, monitor changing behavior and maintain compliance without disrupting mission operations.
That’s especially important for government agencies and defense organizations operating under strict regulatory requirements, where maintaining visibility into AI activity is becoming essential for mission assurance. But considering the way AI is propagating through every kind of enterprise, the need for AI observability and governance applies to just about every type of organization.
Observability Lets AI Be AI
Agentic AI promises groundbreaking improvements in productivity, but like any new technology, it comes with risks. Because of its autonomous interaction with systems and data, agentic AI’s risks cover a wide range, from data leakage and privilege escalation to cascading failures and vulnerability to attacks such as prompt injection. And those risks can appear or change in the blink of an eye.
AI observability is crucial to managing those risks and ultimately getting the best bang for the buck from AI deployments. It can let agentic AI be agentic AI. Observability and governance will also help organizations understand what their AI systems are doing, how they’re changing, and whether they are supporting mission objectives safely and securely.
The key is integrating the components of observability into a unified whole, with clear enterprise-wide visibility and centralized control. Agentic AI is not just a collection of individual applications; it’s a new operational environment, requiring continuous visibility, disciplined governance, and an infrastructure designed to keep it operating with confidence long after deployment.
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At SD3IT, we help government and commercial organizations transform emerging technologies into secure, operational capabilities. By integrating best-of-breed technologies with mission-focused architecture, we help customers build resilient IT environments that support AI, data governance, zero trust and the evolving demands of modern operations.