There is no question about it: AI adoption is the hottest topic in 2026. Surprisingly, according to research, Asia-Pacific is the continent with the second-highest AI adoption rate, just after North America.
In every boardroom from China to Indonesia, from Japan to India, stakeholders are being told that this is the decade where building agentic AI and AI adoption will make or break the future.
While AIOps has proven to make a real impact in bottom lines, report has suggested that 68% of businesses still do not clearly understand the impact of AIOps solutions and many have failed during the process.
In our experience building AI agent and AIOps solutions, here are what most organisations get wrong when building agentic AI.
Why do AIOps projects go wrong?
Here are the 6 most common (and expensive) mistakes we see when building AIOps.
1. Technology- vs. Problem-focused
Many AIOps projects start by obsessing over AI features and framework orchestration, without focusing on specific business problems they are solving. The result is technically impressive, but commercially useless.
2. Reasoning failures
Do most AI agents reasons? Not really, they detect patterns and match them. If you present them with a novel incident, an edge case, or a multi-step causal chain, their logic often reveals flows and collapses. What looks like intelligent analysis in a controlled environment becomes nonsense in production.
3. Context mistakes
AI hallucinations are well-documented. However, in AIOps, the consequences go beyond inaccuracy. Agents can become contradictory. Often they forget pre-defined instructions, become confused and re-diagnose resolved incidents. When they forget organizational compliance and guardrails established in previous interactions, their actions can expose the organizations to security concerns.
4. Verbose analysis with no actionable output
Many AIOps outputs are long, well-structured essays. They are accurate but lack clear action steps. Engineers working under pressure do not need a root cause narrative; instead, they want specific, structured next steps.
5. No human feedback loop
AI agents that do not learn from human feedback require constant human oversight and can pose as a security risks for SMEs. When engineers override a recommendation, close an incident differently as suggested, or mark an output as incorrect…these are all signals that build smart operations sustainability.
6. Weak data governance
Training AI agents on inconsistent data allows them to inherit biases and gaps in the foundation. In regulated industries, this could turn into a compliance and liability problem. Weak data governance is the Achilles’ heel of strong AI capability.
What “deep reasoning” actually means
Deep reasoning in agentic AI represents something specific: it’s the ability to dissect a problem, explore multiple solution paths, evaluate evidence, form a hypothesis, test it, and revise it based on the results. Just like how a skilled human engineer would approach a complex incident.
The architectural approach that enables this kind of reasoning is called “Tree of Thoughts” reasoning. Rather than generating a single, linear response, the system branches out, producing different hypotheses simultaneously and evaluating each against available evidence. The final output reflects more than just an answer; it demonstrates the quality of the reasoning behind it too.
The practical benefits are significant, for instance,
- The system challenges its outputs before submitting, reducing errors
- MTTR decreases significantly
- Reasoning traces can be reviewed, challenged, and corrected
- Accuracy increases over time with self-improvement feedback loops
How to build AI agents and AIOps: 6 things to get right
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Start with one high-frequency incident type (that costs)
Focused AIOps has the competitive advantage of solving specific business problems. To build AIOps that works, try to identify the incident type that occurs often, costs the most engineering resources, and has the clearest resolution pattern. Try to build for that, validate the model, then expand.
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Structured reasoning: think, hypothesise, verify, correct, record
To fully take advantage of AI agents, engineer the reasoning loop to think comprehensively: the system should form a hypothesis, find evidence to test it, and record the conclusion and reasoning trace.
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Context enrichment
Every incident offers useful signals: previous alerts, prior remediations, configuration changes, pre-set guardrails, etc., are training opportunities. Build a retrieval architecture that connects past events into future analysis automatically.
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Actionable outputs
Before you build the model, think through what the output format should look like. What does a good recommendation entail? Consider the incident type, affected systems, root cause, recommended actions, ownership, etc., when generating outputs that are immediately actionable.
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Feedback loop: teachable and correctable
Every correction an engineer makes is a training moment. Build a feedback architecture that captures these moments and routes them back into the system. AIOps that compounds on human expertise grow to become more capable over time.
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Architecture independence
AI model landscapes are moving fast. Building AIOps to be model agnostic, capable of running on different LLMs and across cloud and on-prem GPU configurations is the most future-proof approach.
A production AI agent/AIOps that works – Reason by RE:FORM
What is REASON?
REASON is an on-premises AI deep reasoning and response platform that sits between your existing alerting stack and your ITSM workflow.
Unlike conventional AIOps tools that are cloud-first, data-hungry, and platform-dependent, REASON thinks through incidents the way your own engineers would.
Whether it’s about handling new situations, incomplete data, or multi-variable failures, REASON applies deep multi-step reasoning and turns solutions into actionable remediation.
What can REASON do?
• On-Premises Deployment: Data and response context stay entirely within your environment. Designed for regulated industries where data sovereignty is a hard requirement.
• Tree of Thought Deep Reasoning: Multi-step reasoning that decomposes incidents into verifiable hypotheses, cross-validates evidence, and generates structured conclusions, even for novel, incomplete, or multi-variable scenarios.
• Alert-to-Ticket Closed Loop: Ingest signals from your alerting platform, perform deep reasoning, and write traceable, auditable, complete recommendations directly into your ITSM workflow.
• LLM & GPU Agnostic Architecture: Swap language models freely as the AI market evolves, start with minimal hardware and only scale GPU capacity when event volume demands it. No vendor lock-in at any layer.
• Human Feedback & Self-Improvement: Capture engineer corrections and confirmations as a feedback loop. Similar incidents become progressively faster and more consistent to resolve. The platform continues to get smarter from your team’s expertise.
• Lightweight Integration via MCP, API, A2A: Connect to existing monitoring, SIEM, and ITSM systems through controlled, permission-bound protocols. No data migration required.
REASON is your engineers’ most trusted partner and offers your team an agentic solution that remembers, thinks, and improves as time goes on. For enterprise teams navigating the complexity of building agentic AI, REASON offers a frictionless solution, with uncompromising security.
