There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am.
Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are.
Agentic AI is introducing a new class of production dependency, requiring Ops teams to apply the same rigor they already use for critical infrastructure, services, and distributed systems.
Some "Agentic" AI Is Just a Black Box with a Fancy Name
Rule-based automation dressed up with a large language model "frontend" is going to fail like rule-based automation. The logic under the hood is still deterministic, it just got a fancier user interface: if X happens, do Y.
If conditions arise that no one anticipated - suddenly that rule fails, either loudly or silently, probably without any of the telemetry your ops team needs to figure out what happened.
That distinction is critically important to anyone who will be waking up at 3 am to answer questions about uptime, latency, and incident response. At least with a deterministic system that breaks you can debug it. Failing to debug what you assumed was reasoning isn't just uncomfortable - it also means you literally can't put an SLO around something.
True agentic systems operate differently. They run towards goals using available actions and current context, then replan as the world changes around them. That's very different to monitor than your typical request/response service.
In production, mature agentic systems typically combine deterministic orchestration, policy enforcement, and goal-directed reasoning rather than relying solely on autonomous planning.
3 Observability Questions to Ask About "Agentic" Platforms
From an APM perspective, that difference in behavior raises some open questions, for which the tooling isn't quite ready yet:
- Traceability of reasoning, not just requests. Distributed tracing tells you where a request went. It won't tell you why an agent picked that path when presented with three others. Without understanding the agent's chosen goal, the context it had available, and why it picked the action it did, you're debugging a black box.
- Performance baselines agents can change their plan midway through execution. That means their behavior doesn't map 1:1 to stateless microservices. Latency and error patterns will shift with reasoning paths in ways traditional alerting won't catch. Static thresholds and historical comparisons become far less meaningful. Performance baselines become more dynamic because execution paths vary according to context, available tools, memory, and planning decisions rather than following a single deterministic workflow.
- Failure attribution through multi-agent workflows. Specialized, constrained agents passing work to other agents is solid system design… but it's also a distributed system. When the final output is wrong, which agent "owns" the failure? If you don't have clearly scoped agents and per-agent logging you'll be replaying transcripts looking for root cause.
- Hallucination is another incident category. Hallucinations aren't going anywhere with today's LLMs. The real question is whether they're caught before hitting production via prediction window guardrails, domain-specific fine-tuning, and human approval workflows or whether they show up in your CS dashboard with no explicit alert to catch them. Hallucination risk remains an inherent characteristic of current-generation LLMs. Production architectures should therefore focus on reducing risk through grounding, constrained generation, structured outputs, policy enforcement, and human approval for high-impact decisions
Governance isn't something you bolt onto your dashboard after the fact.
Most generative AI deployments to date have followed a simple path: turn the model loose and whitelist every output through human review. That doesn't scale. It also isn't really governance. Human review is a manual control placed at the edge of the system, often invisible to the monitoring tools responsible for understanding how the system behaves.
In agentic systems, governance has to be an architectural property. That means logical constraints embedded in decision flows, traceability to the agent responsible for each action, and evaluation against external standards rather than internal behavior alone.
Governance should be observable in the same way as application behavior, with policy decisions exposed as measurable operational signals rather than hidden implementation details.
For monitoring teams, that architecture is what makes agentic AI observable. No amount of fancy widgets matters if you're working with a system that only surfaces governance decisions as a checkbox for a human QA team.
Model hosting matters for this too. Off-the-shelf, public-model hosting means your telemetry is subject to another company's infrastructure, monitoring stacks, and violation of their terms of service changes overnight — a new risk vector for your compliance team to follow. Private-hosted, domain-specific models keep that surface area inside your infrastructure perimeter where your team already knows how to monitor it.
Organizations should understand where inference occurs, what telemetry is exposed, how data is retained, and what operational dependencies exist between their platform and the model provider.
Questions to Ask Before Agentic Systems Join Your On-Call Rotation
If you're an operations or reliability team responsible for production systems, your org will probably soon be asked to support an "agentic" platform on your on-call rotation. Before that happens, make sure you get clear answers to the following:
- Does the system fail into a fallback rule when it encounters an input it wasn't trained for, or does it attempt to reason toward a new plan? If the latter, which is easier for your team to detect and alert on?
- How does the platform support tracing agent decisions? Tracing requests and responses is standard practice. Can you also see the goals, context, and selected actions that led to each output?
- How are hallucinations detected/prevented before hitting customers? Equally importantly, what signal does your monitoring infrastructure see when this "guardrail" blocks a decision from executing?
- If this agent is part of a multi-agent workflow, can you attribute errors or bad outcomes to a specific agent? Or does responsibility get lost in the handoff pipeline?
- Is governance part of the decision-making layer itself? Or is it only applied once, as a last step after the agent produces an output for human review?
There's Also an Ops Cost Dimension to This
Aside from thinking about APIs and latency, teams that care about infrastructure costs should consider the operational expense side of this too. Generative AI APIs that bill by token immediately turn every retry, re-plan, and re-iteration into an additional cost. Guess what kinds of workloads iterate aggressively? AI with a lot of task complexity. As task complexity increases, so do the number of iterations required to reach a useful result. The marginal cost of the system therefore rises in direct proportion to the difficulty of the problems it is expected to solve at scale.
Owned, specialized models behave more like traditional infrastructure from a cost perspective. Capital expense up front, stable marginal price. Oh, and — not incidentally — you're working with a system your team can actually instrument, since you host it.
Self-hosted specialized models shift costs away from per-token API pricing toward infrastructure, GPU capacity, engineering operations, and lifecycle management. While not inherently cheaper, they often provide more predictable economics at enterprise scale.
TLDR for Application Performance Monitoring Teams
Agentic AI is arriving on production systems whether or not the observability tools around it are ready for it. When evaluating platforms that promise agentic capabilities, the teams worth trusting are the ones who've built their architectures so that decisions can actually be traced, alerted on, and root-caused. Ask the platform architecture questions before they show you the buzzwords, and you'll know quickly which vendors are ready for your on-call rotation and which ones are just RPA in a trench coat.