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AI applications are shifting from “answer engines” to “action engines.” The moment an AI system can change production state, open a ticket, reroute traffic, or trigger a remediation, reliability stops being about model-quality conversation, and it becomes about operational discipline. This shift forces teams to rethink not just observability, but how production data becomes a continuous improvement loop
In this post, we’ll look at why AI agents are emerging as the next path forward for AI applications, why observability is essential but only foundational, why the stakes rise sharply for agents, and we’ll introduce you to ARI: an SRE-focused AI agent that demonstrates how production data becomes a continuous improvement loop instead of a postmortem artifact.
AI agents are becoming the default interface for “real work”
For the last two years, most teams have tried to make LLMs useful by putting a chat interface in front of internal knowledge. That phase was valuable, but it’s not the best source for long-term leverage.
The bigger shift is toward AI that can develop plans, call tools, persist state, and operate over time; designed to pair well with human-in-the-loop judgment. AWS CEO, Matt Garman, frames the next wave of enterprise value as coming from agents rather than chatbots or copilots, with agents described as long-running digital workers that learn organizational preferences and collaborate at scale.
I think this is directionally correct, but I think most teams underestimate what that implies for production engineering. When an AI system “does work,” it also inherits all the failure modes of distributed systems, the risk profiles of automation, and the governance requirements of enterprise software. Operational AI agents are indeed a product shift, but with that you’ll need to shift your operational and learning mechanisms as well.
Observability for AI applications is essential, but only foundational
If you’re shipping AI features (or any software for that matter), you absolutely need observability. Full stop. For AI, you need to know what prompts were used, what tools were invoked, what context was retrieved, what responses were produced, how long everything took, how costs behaved under real traffic, etc. You also need the “classic” signals: infrastructure health, service performance, deploy history, and dependency behavior.
But observability is not the same as reliability. Observability tells you the truth of what happened. Reliability is what you do with that truth to improve your systems.
That gap is exactly what we emphasized in our “Beyond Observability” webinar: it’s possible (and very common) to detect problems and still fail customers because you cannot translate signals into action quickly, safely, and consistently.
At InsightFinder, we believe that moving past just having “observability” and into continuous improvement workflows is what helps teams quickly mitigate, fix, and predict reliability issues. In other words, observability is the foundation. But the structure you need on top is a workflow that turns production evidence into decisions, then decisions into actions, and then actions back into learning.
LLM reliability matters, but agent reliability raises the stakes
Current AI reliability trends focus on LLM correctness (evals, hallucinations, guardrails). However, the agent era increases the blast radius; an unreliable agent, unlike a chatbot, can cause major engineering disruptions like paging the wrong teams, cascading retries, or pushing bad mitigations.
Agents are described as autonomous, long-running, and scaling across the organization. The fact that deploying early agents requires rebuilding foundational components like identity, policy, security, memory, observability, and drift detection reveals the hidden reliability challenge: the shift to agents means dealing with full production systems, not just models behind an API.
So yes, “LLM reliability” is necessary but it’s also insufficient. Agent reliability encompasses LLM behavior plus tool permissioning, state integrity, workflow correctness, change management, and safe automation patterns. The operational failure surface area multiplies, and so do the failure modes.
Beyond detection with continuous improvement workflows
When people hear “AI observability,” they often picture dashboards and traces. Those are important. But the higher-value pattern is in closed-loop feedback: production signals drive diagnosis, diagnosis drives mitigation, and mitigation outcomes become training data that makes the whole system better the next time.
In our webinar, we demonstrate continuous improvement workflows for production systems in the AI era. That includes using reinforcement learning to improve underlying AI models, using training data derived from real-world feedback loops.
This is also the point where agents can become a natural interface for reliability work. Once you accept that reliability is a workflow, you want a system that can accelerate carrying context across steps, operating at incident speed, and executing repeatable actions safely.
ARI: an SRE-focused AI agent built for repeatable, context-dependent work
To make this real, we built ARI: an AI agent tasked with SRE operations.
SRE work is full of tasks that are repeatable, time-consuming, and deeply context-dependent. There’s a pattern to the work: triage, correlation, initial mitigation, escalation, and documenting what happened. The context dependency is the part that breaks most “AI assistants.” The right next step depends on your stack, your naming conventions, your deploy practices, your traffic shape, and your team’s tolerance for risk.
ARI is an agent designed to help teams understand what’s breaking across their stack with real context, then go beyond detection into workflows that mitigate, fix, and surface early signals so teams can act before customers are impacted.
In practice, this is the difference between AI that can summarize an incident and AI that can participate in the incident response process. It’s also the difference between “answers” and “actions.” ARI supports on-call triage and safe automation, including human-in-the-loop or automatic actions.
Production data is the training data you actually need
There’s a quiet truth every reliability engineer learns early on: production is where your assumptions go to die.
The same is true for AI systems. Your offline test sets and synthetic evals help, but production usage contains the edge cases you couldn’t predict (see: McDonald’s rolls back AI drive through ordering systems).
If you want reliable AI agents, you can’t treat production telemetry as a forensic record that you only examine after an outage. You need to treat it as your richest input stream for improvement.
ARI demonstrates how that’s done by converting production evidence into training data and model improvements, using user-feedback-driven reinforcement learning to fine-tune models with real-world usage. That’s a model all reliability teams managing AI will need to adopt. ARI helps them adopt the reliability workflows they need: ship with guardrails, monitor real behavior, capture feedback, and continuously improve, tightening the loop as agents raise the stakes.
Agents that learn your business context will win
Enterprises need AI that understands their specific domain. Generic models are surprisingly capable, but they don’t know your business’s incident history, service boundaries, runbooks, internal language, and what “normal” looks like. For an AI agent to be useful in production, it must safely and incrementally learn these elements.
That’s why we built ARI for continuous self-learning and domain customization, using production observability data and your feedback to enhance reliability. The “agent era” requires not just better completions but better context, which is gained by operating within the real system, with guardrails, and capturing feedback for training data.
If you’re building agentic systems, remember: observability gives visibility, but workflows ensure reliability. The agent era favors teams that can swiftly move from detection to action to learning while maintaining safety, governance, and speed. You don’t have to choose between speed and reliability; you can design a production loop that continually improves.
See it for yourself
Request a demo to see how an SRE-focused AI agent performs repeatable, context-dependent operational work, using production data as a training signal. We’ll show you how ARI builds reliable, domain-aware AI, continuously improving models with real production behavior.
