Reliability Is the Real Test of Agentic AI

For the last two years, the industry has asked one question: are AI agents capable enough to handle real work? We can stop asking. We know they can, but we need to be laser-focused on whether we can identify when an agent is about to make a costly mistake – and whether we can stop it before it happens.

The worst failures in production don’t typically look like a clean model error. An agent can complete every API call successfully and still work from stale context, retry a tool that’s failing, or head toward an action that breaks a rule. Reliability is what drives whether or not an agentic AI program makes it out of the pilot stage. 

According to “The state of AI in 2025: Agents, innovation, and transformation,” a McKinsey survey, 62% of organizations are experimenting with AI agents, but only around ten percent are not scaling in any of these functions. Showing coworkers an agent works is easy. Operating one safely, across real data and connected systems, is not.

Why Agentic AI Compounds Risk

Agents combine probabilistic reasoning, tool use, and some degree of autonomy. While this makes them useful, it also sets up companies for errors that compound faster than they would in a more traditional application. 

Context

Agents can only work from the context provided. Therefore, if that context is incomplete, outdated or erroneous, a bad interpretation early on carries through to every subsequent step. A flawed chatbot reply is annoying. A flawed interpretation that changes an access permission or touches infrastructure is an incident. It’s important  to know whether the agent used the right evidence, followed policy, and stayed within an acceptable blast radius if something did go wrong.

Knowledge bases

Agents also plug into knowledge bases, ticketing systems, and payment platforms; each connection widens the attack surface. An agent might call the wrong tool, call the right tool in the wrong order, or act on instructions hidden in retrieved content. These are recognized failure modes, and they include confabulation and security vulnerabilities that emerge from the process of how agents chain tools and context together. 

A green dashboard can lie to you: infrastructure looks fine while an agent quietly queries the same tool over and over. This is an early sign of drift rather than a normal outage.

Non-determinism

Non-determinism makes incident response harder, too. A traditional service failure can usually be reproduced with a request ID and a known software version. An agent run depends on the model version, the documents it retrieved, the tool outputs it got back, and a chain of intermediate decisions. Without a record of what the agent received and attempted, root-cause analysis and governance both get much harder.

Cost and latency

Cost and latency tell the same story from another angle. A sudden jump in token use or retries can signal a bad plan or a loop, even if the user eventually gets an answer. Inference costs have fallen sharply over the past few years, and cheaper inference makes it easier to ignore inefficient behavior until that behavior is repeated across thousands of workflows. Treat cost, latency, and retries as reliability signals – not just finance metrics.

Decentralized AI Still Needs Centralized Visibility

Decentralization fails without clear guardrails. Put workflow and escalation ownership with the frontline teams, but keep identity, access, and incident response at the enterprise level. A finance team can tell the difference between a legitimate invoice exception and an improper payment decision in a way a generic benchmark never will. In the same survey, McKinsey found that organizations reporting real AI impact were nearly three times more likely to have redesigned their workflows, rather than bolting AI onto what already existed.

That said, the tradeoff is real: ownership gets fuzzy fast when an incident crosses systems. The fix is a shared operational view of every agent, its tools, its data access, and its incident history.

Distributed architectures make it easy to lose the full picture when something breaks. Many teams can see token volume and cost, but they cannot see whether an agent actually achieved the intended outcome safely. When records across a workflow live in different places, teams end up chasing symptoms instead of causes. 

Standardized telemetry signals, like model identity and tool calls, can help. Behavioral baselines, like the normal number of steps in a workflow, are also necessary in order to identify useful persistence from a system that’s stuck. Evaluation isn’t a one-time gate before launch.It’s a continuous loop. 

What Reliable AI Actually Looks Like in Production

Reliable AI is about managing mistakes, not avoiding them. Teams need visibility into behavior; alerts when performance drifts; and a containment plan for when things go wrong. Most importantly, every agent needs clear guardrails around access and autonomous actions. They also need human sign-off. 

Start with low-risk actions that can be reversed. Keep consequential ones, like production changes and financial transactions, behind real controls. Consequential workflows should be able to be reconstructed after-the-fact, including the context the agent retrieved, the tools it called, the approvals it got, and whether the outcome was actually correct.

Traditional service-level objectives need to stretch to cover agent quality and safety, too; they include validated task-success rate, policy-compliance rate, human-escalation rate, cost per successful task, and how often undesirable outcomes happen. Thresholds should vary by use case. An internal knowledge assistant can tolerate a different error profile than an agent touching regulated data.

The most useful reliability systems learn to spot the conditions that tend to come before a failure, like an abnormal jump in retries or a path that’s historically led to human overrides. A low-risk workflow might trigger an automatic fix. A higher-risk one should pause and route the decision to an authorized person. The goal is not autonomous action for its own sake. The goal  is faster, safer action when the evidence supports it.

AI SRE Closes the Loop

This is where AI SRE comes in. An AI SRE agent can assemble an incident timeline, compare current behavior against past incidents, and prepare a recommended action, while the organization keeps controlled remediation for reversible tasks and human approval for anything consequential.

AI adoption is moving fast. But adoption isn’t the same as operational maturity. The enterprises that scale agentic AI successfully won’t necessarily be the ones running the most capable model in isolation. They’ll be the ones who can see how their agents behave across the business, catch early signs of drift, and step in before a small error becomes a customer, security, or compliance event. 

That’s the role AI SRE is filling: connecting decentralized innovation with centralized visibility and turning agentic AI into a system the enterprise can trust.