How to build a smart and safe roadmap to Agentic AI (without breaking everything)
By Krishna Sai (pictured), Chief Technology Officer, SolarWinds
Move fast and (don’t) break things. There’s a curious contradiction emerging that is reframing Silicon Valley’s favorite mantra. According to the latest CEO survey by IBM, almost two-thirds (64%) of responding global CEOs say making bigger bets on Artificial Intelligence (AI) is essential to staying ahead of competition – yet many admit they are already relying on technologies they don’t fully understand.
AI agents – systems able to plan, act and make decisions with minimal human input – are quickly becoming more than just an idea. They are being deployed in every sector. As this accelerates, so does a lack of awareness and visibility into the many moving parts. Australian boards are recognizing a new class of system-level uncertainty, and CEOs are placing a premium on trust, with two-thirds of participants now saying customer trust matters more to long-term success than product innovation.
The challenge is that jumping straight into full autonomy without a solid foundation is risky. Poorly governed or designed agentic AI can lead to uncertain decision-making, silent system failures and unmanageable complexity. What’s needed is a phased approach, one built on safe experimentation, full-stack observability, predictive operations and compliance-driven control.
Why agentic AI needs a phased approach
Unlike traditional large language models, which respond in a single turn, agentic AI employs strategies such as chain-of-thought reasoning to break down tasks, evaluate intermediate outcomes and adjust actions. Such freedom is powerful, but it also amplifies risk. A mistuned agent can throttle systems, misroute traffic or take unintended actions in seconds.
Gartner now expects more than 40% of agentic AI projects to be cancelled by 2027, largely because organizations underestimate costs, complexity and risk controls. Early movers who succeed share three habits: they move in phases, design for observability from the start and embed governance into existing assurance structures.
The same principles guiding how we monitor complex, hybrid IT infrastructures – tracking resource usage, surfacing anomalies, establishing baselines and enabling fast root-cause analysis – should apply to how we manage agentic AI.
Define the anatomy of the agent – and how you’ll observe it
Before deploying agents, define not just what they will do but how you’ll see and interpret their behavior. For example, how does the agent choose objectives? Can you inspect that logic? Are API calls, tool usage and failure modes logged and reviewable? Is intermediate reasoning visible in real time?
At SolarWinds, we’ve learned from decades of infrastructure monitoring that system reliability depends on what you can observe, measure and understand. Agentic AI is no exception. By making observability part of the agent’s scaffolding – not just the infrastructure layer – you can help ensure every action is trackable, explainable and reversible if needed from the start.
Design observability into every system layer
Agentic AI doesn’t run in isolation. It relies on complex data flows, API layers and data pipelines. This interdependence makes observability an architectural principle. Without it, agentic AI becomes risky and reactive rather than adaptive.
Organizations with strong observability practices already understand this. Visibility across logs, metrics and traces allows for diagnosis of anomalies, optimization of performance and issue containment. Those same capabilities are critical when deploying AI Agents – only now you’re monitoring behavior, not just systems.
Govern what you can see and only trust what you can audit
Strong AI governance requires real-time introspection. This includes detecting when agents act outside policy, alerting when performance or outputs degrade or deviate from expected norms and supporting investigations with detailed audit trials. Put simply, if you can’t explain what your agent just did, it shouldn’t be running in production.
Layering in predictive intelligence
Once systems are observable, they can become predictive. Real-time telemetry data feeds smarter recommendations, trend detection and forecasting.
Predictive systems allow teams to stay ahead of operational risk while also generating new feedback for improving both infrastructure and AI agents. Agents can detect performance degradation, such as CPU queues, and auto-scale capacity, preventing a customer-visible slowdown. This closes the loop between agent behavior and infrastructure performance – enabling faster recovery, better uptime and continual improvement.
The future of agentic AI won’t be built on blind faith but on measurable trust. That requires transparency, traceability and real-time insights into what agents are doing and why. It’s about having the right scaffolding in place for safe autonomy.
So sure, move fast. But make sure you observe everything.
