For much of the past decade, artificial intelligence answered questions. It summarized documents, drafted text and returned suggestions a person could accept or ignore.
A newer generation of AI systems is beginning to do something different: act.
Autonomous agents can complete multi-step tasks, use tools, move through websites and operate across digital workflows on a user’s behalf. That shift creates a much higher bar for trust. A chatbot that gives a weak answer may frustrate a user. An agent that takes the wrong action in a live account can create a real operational problem.
That is why the next stage of AI adoption is not only about whether agents can perform tasks. It is about whether teams can evaluate, monitor and explain what those agents do before and after they are deployed.
The market reflects that shift. The global agentic AI market reached $7.29 billion in 2025 and is projected to reach $139.19 billion by 2034, according to Fortune Business Insights. But as adoption grows, so does the need for infrastructure that can test agent behavior before it reaches customers.
Sidhesh Badrinarayan, a senior software engineer and technical lead specializing in AI infrastructure, has worked on that challenge inside production agent systems. His focus is the evaluation layer: the systems that help determine whether an autonomous action is reliable enough to take in a real environment.
The gap between demos and deployment
The distance between an impressive demo and a dependable production system is where many AI projects struggle.
A demo can show an agent completing a task once under controlled conditions. Production requires the same system to perform reliably across messy accounts, unexpected edge cases, changing websites and long multi-step workflows. That is a different engineering problem.
For autonomous systems, the risk is also different. An agent that can change settings, trigger workflows or operate inside user accounts needs stronger controls than a model that only generates text.
That makes evaluation more important. Teams need to know not only whether the final answer looks right, but whether the agent chose the right tools, called them in the right order and used the correct inputs along the way.
Why “looks right” is not enough
AI systems can appear convincing while still making mistakes. That problem becomes more serious when the system is allowed to act.
Traditional review methods are often too limited for agentic systems. A human reviewer may be able to read a sample of outputs and judge whether they seem reasonable. But that approach does not scale well across thousands of long, multi-turn agent runs.
Badrinarayan has written about this shift in The Era of Vibe Checking AI Is Over: Welcome to Eval-Ops, arguing that systematic evaluation is becoming a core part of production AI work.
That idea is especially relevant for agents. A single wrong tool call early in a workflow can affect every later step. If teams only review the final output, they may miss the decision that caused the failure.
More rigorous evaluation looks at the whole process: what the agent saw, what it inferred, which tool it selected, what information it passed to that tool and whether the action matched the user’s intent.
Testing agents before customers are affected
For production AI teams, one goal is to move failure earlier.
Instead of discovering problems after an agent has acted on a real account, engineers can test behavior in simulated environments. Those environments can recreate realistic workflows without exposing customers to mistakes.
This is particularly important for agents that operate across websites, business tools or account settings. A system may need to handle missing information, changed interfaces, conflicting instructions or unexpected account states.
Simulation and tool-mocked testing can help teams evaluate whether an agent calls the right functions with the right inputs before it is allowed to operate in production. It also allows teams to test multi-turn behavior, where the risk may emerge only after several steps.
That kind of testing does not eliminate risk. But it gives teams a better chance to find dangerous edge cases before users encounter them.
Observability as the record of what happened
Once an autonomous system is deployed, teams need a way to see what it actually did.
That is where observability becomes central. The market for AI agent observability is projected to grow from $0.4 billion in 2025 to $7.1 billion by 2035, according to Astute Analytica. The growth reflects a practical need: as agents take more actions, companies need better tools to trace and monitor those decisions.
For agents, observability is not just a dashboard. It is the record that helps engineers understand what the system saw, what it decided and what action it took. Without that record, troubleshooting becomes guesswork.
Observability also matters for accountability. If a user reports that an agent changed the wrong setting or misunderstood an instruction, a team needs to reconstruct the sequence of decisions that led to the result.
Badrinarayan has also explored related issues in why language models rewrite history and how multi-agent systems can help restore it, focusing on the risks that emerge when AI systems lose track of state or produce confident but untraceable errors.
The infrastructure behind autonomous AI
As agents move from suggestions to actions, the infrastructure around them becomes more important.
That infrastructure includes evaluation systems, simulations, monitoring tools, audit trails, tool-use checks and escalation paths for uncertain cases. These are not as visible as model launches or product demos, but they may determine whether autonomous systems can be trusted in business settings.
The next phase of AI adoption is likely to depend less on whether agents can perform a task once and more on whether companies can prove that agents perform it reliably across many conditions.
That is the challenge facing teams building autonomous systems now. Capability alone is not enough. The systems need evidence, monitoring and controls that can support real deployment.
For AI agents, the future will not be decided only by what they can do. It will also be decided by how well companies can evaluate what they are about to do, observe what they have done and intervene when the system is not ready to act on its own.