Announcing the Harness Human-Aware Change Agent | Harness Blog

At Harness, our story has always been about change — helping teams ship faster, deploy safer, and control the blast radius of every modification to production. Deployments, feature flags, pipelines, and governance are all expressions of how organizations evolve their software.

Today, the pace of change is accelerating. As AI-assisted development becomes the norm, more code reaches production faster, often without a clear link to the engineer who wrote it. Now, Day 2 isn’t just supporting the unknown – it’s supporting software shaped by changes that may not have a clear human owner.

And as every SRE and on-call engineer knows, even rigorous change hygiene doesn’t prevent incidents because real-world systems don’t fail neatly. They fail under load, at the edges, in the unpredictable ways software meets traffic patterns, caches, databases, user behavior, and everything in between. 

When that happens, teams fall back on what they’ve always relied on: Human conversation and deep understanding of what changed.

That’s why today we’re excited to introduce the Harness Human-Aware Change Agent — the first AI system designed to treat human insight as operational data and use it to drive automated, change-centric investigation during incidents.

Not transcription plus RCA. One unified intelligence engine grounded in how incidents actually unfold.

📞 A Quick Look at Harness AI SRE

The Human-Aware Change Agent is part of Harness AI SRE — a unified incident response system built to help teams resolve incidents faster without scaling headcount. AI SRE brings together the critical parts of response: capturing context, coordinating action, and operationalizing investigation.

At the center is the AI Scribe, because the earliest and most important clues in an incident often surface in conversation before they appear in dashboards. Scribe listens across an organization’s tools with awareness of the incident itself – filtering out unrelated chatter and capturing only the decisions, actions, and timestamps that matter. The challenge isn’t producing a transcript; it’s isolating the human signals responders actually use.

Those signals feed directly into the Human-Aware Change Agent, which drives change-centric investigation during incidents.

And once that context exists, AI SRE helps teams act on it: Automation Runbooks standardize first response and remediation, while On-Call and Escalations ensure incidents reach the right owner immediately.

AI SRE also fits into the tools teams already run — with native integrations and flexible webhooks that connect observability, alerting, ticketing, and chat across systems like Datadog, PagerDuty, Jira, ServiceNow, Slack, and Teams.

🌐 Why We Built a Human-Aware Change Agent

Most AI approaches to SRE assume incidents can be solved entirely through machine signals — logs, metrics, traces, dashboards, anomaly detectors. But if you’ve ever been on an incident bridge, you know that’s not how reality works.

Some of the most important clues come from humans:

• “The customer said the checkout button froze right after they updated their cart.”
• “Service X felt slow an hour before this started.”
• “Didn’t we flip a flag for the recommender earlier today?”
• “This only happens in the US-East cluster.”

These early observations shape the investigation long before anyone pulls up a dashboard.

Yet most AI tools never hear any of that.

The Harness Human-Aware Change Agent changes this. It listens to the same conversations your engineers are having — in Slack, Teams, Zoom bridges — and transforms the human story of the incident into actionable intelligence that guides automated change investigation.

It is the first AI system that understands both what your team is saying and what your systems have changed — and connects them in real time.

🔍 How the Human-Aware Change Agent Works

1. It listens and understands human context.

Using AI Scribe as its conversational interface, the agent captures operational signals from a team’s natural dialogue – impacted services, dependencies, customer-reported symptoms, emerging theories or contradictions, and key sequence-of-events clues (“right before…”).

The value is in recognizing human-discovered clues, and converting them into signals that guide next steps.

2. It investigates changes based on those clues.

The agent then uses these human signals to direct investigation across your full change graph including deployments, feature flags or config changes, infrastructure updates, and ITSM change records – triangulating what engineers are seeing with what is actually changing in your production environment.

3. It surfaces evidence-backed hypotheses.

Instead of throwing guesses at the team, it produces clear, explainable insights:

“A deployment to checkout-service completed 12 minutes before the incident began. That deploy introduced a new retry configuration for the payment adapter. Immediately afterward, request latency started climbing and downstream timeouts increased.”

Each hypothesis comes with supporting data and reasoning, allowing teams to quickly validate or discard theories.

4. It helps teams act faster and safer

By uniting human observations with machine-driven change intelligence, the agent dramatically shortens the path from:

What are we seeing? → What changed? → What should we do?

Teams quickly gain clarity on where to focus, what’s most suspicious, and which rollback or mitigation actions exist and are safest.

🌅 A New Era of Incident Response

With this release, Harness is redefining what AI for incident management looks like. 

Not a detached assistant. Not a dashboard summarizer. But a teammate that understands what responders are saying, investigates what systems have changed, connects the dots, and helps teams get to truth faster.

Because the future of incident response isn’t AI working alone. It’s AI working alongside engineers — understanding humans and systems in equal measure.

Book a demo of Harness AI SRE to see how human insight and change intelligence come together during real incidents.

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