AI agents are moving into production faster than any prior enterprise technology, and most of them run effectively blind. The team can see that an agent is deployed. It cannot see what the agent decided, why it decided it, whether the quality is holding, or what the last hour cost. That gap is the difference between a system you operate and a system you gamble on.

Shipping an agent and operating an agent are different problems

Getting an agent past a pilot is one milestone. Keeping it working in production for years is another, and it is the one most teams underinvest in. Gartner projects that over 40 percent of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. Two of those three failure modes are visibility problems. You cannot control a cost you do not measure, and you cannot prove value you cannot trace.

Observability is the ability to see what an agent did, why it did it, and whether it is still working correctly. For a deterministic service you can often infer that from uptime and error rates. An agent is different. It makes open-ended decisions, calls external tools, and produces output that can degrade quietly without ever throwing an error. Standard application monitoring will tell you the agent is running. It will not tell you the agent is wrong.

What to instrument in a production agent

Five layers make an agent observable. Each answers a specific question a CTO will eventually be asked, and each has a predictable failure mode when it is missing.

1. Decision logging and tracing

Tracing is a step-by-step record of everything the agent did to reach an output: the prompts sent to the model, the tools and APIs it called, the data it read, and the intermediate reasoning, all linked into one timeline for a single request. Decision logging captures not just the final answer but the path to it. Without it, every incident becomes archaeology. When a customer reports that an agent issued a wrong refund or a bad recommendation, you need to reconstruct which inputs, tool calls, and model responses produced that action. If you did not log the trace, that information is gone. With it, root cause takes minutes instead of days, and you can replay the exact sequence to confirm a fix.

2. Continuous evaluation against success criteria

An evaluation harness, or eval, is an automated test that scores agent output against defined success criteria: is the answer factually correct, does it follow policy, did it complete the task. Running evals only before launch tells you the agent worked once. Production quality moves. Continuous evaluation samples live traffic and scores it on a schedule, so a drop in correctness surfaces as a metric rather than as a wave of complaints. This is where you encode what correct means for your use case, whether that is grounding against source documents, adherence to a defined workflow, or a human review score. Without a running eval, quality is an anecdote, not a number.

3. Drift and anomaly detection

Drift is a gradual change in agent behavior or output quality over time with no change to your code. It happens because the world around the agent moves: the underlying model is updated by the provider, the distribution of user requests shifts, a data source changes format, or a prompt that worked last quarter no longer covers new cases. Drift detection watches your eval scores, output patterns, and tool-call distributions for statistically meaningful change, and alerts when today does not look like the established baseline. Without it, degradation is invisible until it crosses a threshold a customer notices. A provider-side model update can move behavior overnight, which is exactly the kind of shift a baseline catches and a one-time spot check does not.

4. Cost and token monitoring with alerts

Models bill per token, the unit of text they read and write, and inference cost is what you pay each time the model runs. Agent architectures multiply this: one user request can trigger many model calls, tool calls, and retries, so cost per task is variable and hard to predict. This is not hypothetical. Uber reported burning through its entire 2026 budget for AI coding tools in four months, with power users running thousands of dollars a month, then responded with a per-employee spending cap. Tesla imposed a $200-per-week limit on employee AI spending after engineers were consuming thousands of dollars of tokens each week. The lesson for an autonomous agent is sharper: an agent with a retry loop and no cost ceiling can run up a bill with no human in the loop at all. Per-agent and per-task cost tracking, with alerts and hard caps, is what turns an unbounded liability into a managed line item.

5. Human escalation and kill-switch paths

Observability is only useful if someone can act on what it shows. Every production agent needs defined thresholds at which it stops acting on its own and routes to a person: low confidence, a high-value transaction, a policy-sensitive action, or an anomaly the system flagged. It also needs a kill switch, a way to halt or roll back the agent immediately when a trace, an eval, or a cost alert shows it is misbehaving. Gartner recommends exactly this pairing of continuous monitoring with circuit breakers and rapid rollback for autonomous agents. Without an escalation path, the agent takes irreversible actions it should have handed off. Without a kill switch, your only option during an incident is to hope it resolves on its own.

Instrument before the incident, not after it

These five layers are not add-ons you bolt on when something breaks. They are the operating surface of the agent, and they are cheapest to build while the agent is being built. The market is converging on this. Gartner projects that investment in LLM observability, monitoring that goes beyond response times to track model-specific signals like hallucinations, bias, and token use, will reach 50 percent of generative AI deployments by 2028, up from 15 percent, and that 40 percent of organizations deploying AI will use dedicated AI observability to monitor model performance by 2028. An R and D partner builds this layer as part of the agent, decision tracing, a continuous eval harness, drift and anomaly detection, cost and token monitoring, and human escalation with a kill switch, so the agent becomes something you operate for years rather than something you watch and hope. If getting an agent to production is the first problem, seeing what it does once it is there is the one that decides whether it stays.

Sources

  1. Gartner, "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027," 2025. Link.
  2. Gartner, "Gartner Predicts by 2028, Explainable AI Will Drive LLM Observability Investments to 50% for Secure GenAI Deployment," 2026. Link.
  3. Gartner, "Gartner Predicts 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028," 2026. Link.
  4. Fortune, "Uber Burned Through Its Entire 2026 AI Budget in Four Months," 2026. Link.
  5. Electrek, "Tesla Caps Employee AI Spending at $200 Per Week Except for Grok," 2026. Link.

Next Steps

The decision in front of you is whether your agents are observable enough to operate, or whether you are running them on faith. If you cannot produce a trace of what an agent did last week, score its quality on live traffic, or see its cost per task, that is where to start. AI and Automation is where we build the observability layer into production agents, and you can contact our team to review what your current agents do and do not show you.