Sponsored Feature We're at the beginning of the agentic era for operations. Current AIOps tools summarize dashboards and surface correlations, but most don't actually investigate incidents. So engineers still spend hours manually investigating complex incidents.

Vinod Jayaraman, co-founder and head of engineering at NeuBird.ai, thinks it's time for that to change. Agentic AI systems can now perform actual SRE investigation work. The companyhas applied agentic AI to production operations. It automatically correlates telemetry data across AWS services without human intervention, surfacing root causes that Jayaraman says save engineers hours.

This marks the first time AI can reason over telemetry like an engineer would. It builds a service map to understand how components connect before starting an investigation, and then explores multiple hypotheses in parallel faster than human experts could.

That's already happening, but now he wants to take things further. "One of the topics that is close to our hearts for this year is how we can close the SRE loop and also get closer to code generation," he says. "We also want to have a reasoning graph that explains how we came up with a root cause analysis (RCA)."

The future is autonomous investigation, not better dashboards

Alert storms and dashboard sprawl are symptoms of architectural scale, not solutions to it. About 95 percent of alerts generated from low-level metric thresholds don't need to be investigated. High CPU usage might be good (a compilation job running) or bad (a runaway process). Context matters.

Humans cannot keep pace with modern telemetry volume. The next stage of evolution is to reduce human dependency on observability, not to throw more tools at the problem. NeuBirdstreamlines incident response by handling the investigation work autonomously.

AI agents must become the first responder to incidents. When incidents are queued up in the background, the system can refine RCA results using reasoning models without time pressure. Low confidence scores (say, less than 60%) trigger additional investigation passes rather than immediate escalation.

Things need to become more intuitive for SREs, DevOps, and Platform Engineering. "We want them to describe the end outcome that they want to avoid, and do so using natural language, which we call semantic monitoring," he says

That means moving beyond simply setting thresholds on CPU or memory. Instead, an SRE might say 'Monitor for any pod failures in this Kubernetes cluster that last more than two minutes'. The system breaks that down into various things that need to be monitored under the hood.

What the future looks like for SREs &DevOps

Instead of stressing out at the battlefront, site reliability engineers and those tasked with application reliability will soon spend at least some of their time supervising AI agents instead.

In fact, while a human stays in control, it's agents all the way down. Supervisor agents can monitor other agents' investigations to keep them on the right track. When an agent gets stuck looking at the same metrics repeatedly, the supervisor redirects its attention elsewhere. It's like having a senior engineer guide a junior through their first incident response.

This agentic support won't replace people, but it will reduce a lot of the correlation work that they currently face when tracking problems across complex distributed systems.

Closing the SRE/DevOps loop

The next step for NeuBird is to go beyond solving immediate problems as a discrete practice by integrating with other parts of the incident response chain. He wants to close the problem resolution loop, where engineers diagnose problems, produce resolutions, update software, and prevent recurrence.

"When you come up with a probable RCA, things might get updated, but that's also often where the ball gets dropped," says Jayaraman. "We want to shift left, getting closer to the development cycle where once the RCA is produced, you're able to continue on and surface what you discovered, passing it on to the next step in the pipeline."

This is where his concept of code generation comes in. In the future, an SRE agent might deliver details of a problem to a coding agent that generates a fix and then writes a pull request to fix an issue.

Explainability and reasoning graphs

As NeuBird.ai explores these broader automation opportunities, trust will be central to getting engineers on board.

Trust remains the limiting factor. Some customers want single, high-confidence root cause analyses rather than multiple hypotheses. Low confidence scores trigger additional investigation passes. For deployment changes versus code changes, the system can apply fixes and verify results.

That's why humans are very much still in this loop. NeuBird is developing guardrails for autonomous approval of simple infrastructure changes (adding a single node for resource starvation, for instance). More complex fixes need a person to check the work and pull the lever.

Security permissions determine how far automation goes. Simple problems might get autonomous fixes within strict guardrails. Everything else stays in recommendation mode, with humans making final calls.

That in turn demands transparency, which is something that the company is building more of into its systems. Users can double-click to see why the system arrived at a particular RCA. The system will show its work: which metrics it checked, what logs it parsed, and which correlations it found.

"We present the solution, but also the path by which we arrived at the solution," says Jayaraman. "We present the queries that were executed to get out the information, which led us to believe that this is what the root cause is."

Consider a message queue overflow scenario. The consumer falls behind. NeuBird AI SRE provides context and recommends scaling out by adding another node. It produces specific Terraform script adjustments.

The product generates an internal confidence score on each RCA. High confidence means a clear answer. Low confidence triggers deeper investigation. Either way, the system presents the queries it executed to reach its conclusion, not just the final answer.

Anyone that has seen two AIs argue with each other will appreciate how fascinating this process is. Hawkeye uses adversarial thinking to sharpen accuracy. Two models analyze the same incident independently. Agreement means high confidence. Disagreement flags uncertainty. The system uses LLMs as judges to evaluate the quality of work done.

Virtual SREs don't need to chat just with each other. Static reports are giving way to conversations, with engineers asking follow-up questions about an RCA, requesting different analyses, or exploring alternative hypotheses. The system learns from each interaction, incorporating feedback to improve future incident analysis.

These reasoning systems are another area that NeuBird will take further. The future vision is for these reasoning graphs to evolve into comprehensive automated runbooks. They will go beyond solving specific problems to address similar issues in the future. That way, operational memory persists even as teams change. And it can continuously evolve, updating runbooks as Hawkeye generates more understanding.

Why this shift is happening now

SREs and DevOps have needed capabilities like these for a while. Cloud complexity has exceeded human cognitive limits. The scale isn't a technology problem anymore. "In a complex piece of software, there are many pieces of code. When they all come together with the variability of your environment and the user traffic that comes in, there is no perfect code," Jayaraman points out.

Talent shortages have compounded the problem. It's hard to scale SRE teams when skills are short.

The demand might have been there for some time, but the capability wasn't. That's changing, as LLM maturity enables reasoning across telemetry data. Models can now process information more quickly while maintaining accuracy. NeuBird's architecture helps here; the company breaks agent tasks into bite-sized chunks, each using a different model optimized for that task. Smaller reasoning models can often produce superior results to large, cumbersome, foundational models designed to do everything.

The evolution of infrastructure as code also makes autonomous operations both feasible and critical. Agents can now address resource starvation problems through Terraform, while changes tracked in GitHub repositories close the operational loop.

Driving adoption

Aside from trust, removing security friction and deployment constraints is critical for this technology. For AI systems to handle production incidents, they need bulletproof security. Hawkeye processes telemetry in real-time without storing it persistently. Customer data stays inside their AWS environment. When reasoning happens, only abstracted metadata reaches Amazon Bedrock.

The trust model relies on read-only permissions for AWS services, protected by AWS IAM. Customers control access through their own trust policies and can revoke permissions instantly.

NeuBird is also easing adoption through flexible deployment options. Half of the company's customers run purely in the cloud, while the rest use virtual private cloud (VPC) or hybrid setups.

The system connects to Prometheus in a customer's VPC or on-premises just as easily as cloud telemetry. From NeuBird's perspective, both look identical.

When those customers get an EC2 instance failure, NeuBird gets there first. The AI agent examines CloudWatch metrics, logs, and configuration changes to understand what happened, accessing telemetry sources through read-only connections. By the time the on-call engineer gets to the issue, there's already a root cause analysis waiting.

That's exciting enough, but the company clearly has great things planned for this year. It's working quickly, too, expecting a declarative definition for the problem resolution pipeline ready by end of Q1. All eyes on NeuBird AI to see what it delivers next.

Sponsored by NeuBird.ai