How reliable are AI agents for production work?

The honest answer: it depends entirely on how they're built.

Foundation large language models (llms) from providers like OpenAI, Anthropic, Google, and Microsoft have reached a threshold where they can reason through complex problems and orchestrate multi-step workflows across external tools. This baseline capability improves with each generation. But "can reason" and "reliable for production systems" are different claims, and the gap between them is where most AI projects fail or succeed.

Production environments are unforgiving. A wrong diagnosis during an incident costs time. A false positive that pages engineers at 3am costs trust. An agentic AI system that confidently produces plausible-but-incorrect root causes is worse than no agent at all. Engineers who've been burned by hallucinations or overconfident recommendations have earned their skepticism.

The question isn't whether foundation models are capable enough. They are. The question is what you build around them.

What foundation models provide

Models like GPT-4, Claude, and Gemini contribute to a good percentage of engineering productivity. They can parse logs, interpret metrics, read code, and synthesize information across formats that would take humans significant time to correlate. They can maintain context across long reasoning chains. They can operate tools through function calls and APIs.

More importantly, they can reason about problems in ways that feel qualitatively different from earlier automation. Given a hypothesis and access to evidence, they can evaluate whether the evidence supports or contradicts that hypothesis. They can generate alternative explanations. They can articulate their reasoning in ways humans can evaluate.

This is the substrate. It's necessary but not sufficient.

Why the substrate isn't enough

Production work has characteristics that foundation models alone don't handle well.

Production context is organizational. A model might know what a Kubernetes OOMKilled error means in general. It doesn't know that your payment service has a known memory leak that the team tolerates because the fix requires a major refactor, or that OOMKilled events on that specific service during batch processing windows are expected. This context lives in runbooks, Slack threads, past incidents, and engineers' heads. Without it, an AI system will investigate problems that aren't problems and miss edge cases that matter.

Production data is distributed across tools that don't talk to each other. An incident might require correlating a metric spike in Datadog with a deployment in ArgoCD, a code change in GitHub, and a configuration update in your feature flag system. Each tool has its own query language, its own data model, its own quirks. A foundation model with generic tool access will either pull too much data (hitting rate limits and burning tokens) or miss the specific evidence that matters.

Production reasoning can't be a black box. When an AI-powered agent concludes that database connection pool exhaustion caused a service degradation, that conclusion is only useful if it can show the evidence: the connection count metrics, the correlated timing with the error spike, the code path that acquires connections without releasing them. Conclusions without evidence aren't actionable. They're guesses that engineers have to verify from scratch anyway.

Production decisions have consequences. Suggesting a remediation that makes things worse isn't a minor inconvenience. Automated rollbacks on false positives create their own incidents. The bar for production-ready AI is higher than in contexts where mistakes are easily reversible.

What makes AI agents actually reliable

Reliability emerges from the engineering frameworks built around the foundation model. These layers are substantial, and they're where the real work happens.

Deep integrations with production tools. Not just API access, but understanding of how each tool works: what queries are efficient, what data is authoritative, how to interpret results in context. Generic tool access treats telemetry data like text. Purpose-built integrations understand that a P99 latency spike means something different than a P50 spike, that trace data has parent-child relationships that matter for causality, that some metrics are noisy by nature and shouldn't trigger concern.

Continuous learning from organizational context. The system needs to ingest and maintain knowledge scattered across documentation, past incidents, Slack channels, and code comments. This isn't a one-time indexing job. Production environments change constantly. Services get deprecated. Ownership transfers between teams. New dependencies get introduced. The datasets that informed decision-making last month might be misleading today.

Validated feedback loops with human-in-the-loop correction. When an engineer corrects agent behaviour or points it in a different direction, that correction should improve future investigations. When an investigation path leads to a confirmed root cause, that path becomes a precedent. This creates a data flywheel where each resolved incident makes the system better at handling similar situations. But the key word is "validated": learning from outcomes that turned out to be correct, not just from agent outputs.

Evidence-backed reasoning with explicit confidence. Reliable agents don't just produce conclusions. They show their work: the hypotheses they considered, the evidence they gathered, why they ruled out alternatives. They distinguish between high-confidence conclusions backed by strong evidence and lower-confidence suggestions that warrant human verification. Effective guardrails ensure the system knows what it doesn't know.

Expanding evaluation coverage. Reliability requires measurement, and measurement requires evaluations that map to real-world outcomes. Not benchmark accuracy on generic tasks, but success rates on the specific types of problems your production environment encounters. Tracking error rates across different incident types, measuring how multi-agent systems perform on cross-domain investigations. These metrics must expand as the system handles more cases. The gap between prototypes and production deployments is often the absence of this rigor.

Enterprise AI security requirements. Production access means access to sensitive systems, logs that might contain customer data, infrastructure that could be damaged by incorrect actions. Reliability includes security: proper access controls, audit trails, data handling that meets compliance requirements.

How can you get the compounding effect

These requirements aren't independent. They compound through iteration.

Better integrations mean the agent gathers more relevant evidence. Better evidence improves the quality of conclusions. Higher-quality conclusions mean more validated feedback. More validated feedback improves future investigations. More successful investigations build the evaluation suite. A richer evaluation suite catches regressions before they affect users.

This is why reliability isn't a feature you add; it's a property that emerges from building the full system correctly. A foundation model with generic tool access will plateau. A system with deep integrations, continuous learning, and expanding evals will optimize itself with every investigation.

The trajectory matters as much as the current capability. A system that's 70% reliable today but improves to 90% over six months is more valuable than a system that's 75% reliable and stays there.

How Resolve AI approaches reliability

Resolve AI is built around this compounding model. The system maintains a real-time understanding of production environments: how services relate to each other, what infrastructure supports them, which teams own what, how deployments propagate through dependencies. This context is the foundation for reasoning that's actually grounded in your systems, not generic patterns.

During investigations, specialized agents work across domains like code, infrastructure, observability, changes to gather evidence and build hypotheses. The system tracks the evidence chain: which data points support which conclusions, what alternatives were ruled out and why. When engineers validate or correct the reasoning, those corrections become part of the system's knowledge.

The result is a system that gets meaningfully better over time. Not because the foundation model improves (though that helps too), but because the organizational context, the validated investigation patterns, and the evidence of what works accumulate with use. This is what makes artificial intelligence reliable for production work: not just capable models, but the engineering to make those models trustworthy in high-stakes contexts.