Devtron Atlas
Your AI SRE That Never Sleeps
Atlas predicts, prevents, and resolves incidents at machine speed — so your SREs can focus on building, not firefighting.
Application Onboarding with Atlas
Atlas assists developers in onboarding a new application by analyzing the repository and helping configure build pipelines, deployment workflows, environments, and application settings.
One instruction to Atlas for app, pipelines, and images all configured automatically
Detects framework, Dockerfile, and configs from the repo no manual setup
Deploys to staging instantly, raises a production approval request simultaneously
Incident Investigation with Atlas
Atlas transforms alerts into actionable insights by correlating deployments, logs, events, and infrastructure signals to identify root causes and recommend actions.
Atlas forecasts CPU throttling on payment gateway 6 hours before it hits prod
Shows full reasoning trail trends analyzed, spike predicted, cause explained
Recommends exact config changes with a before-and-after comparison
One-click approve issue resolved before any user sees it
Cost Optimization & Forecasting with Atlas
Atlas evaluates resource consumption trends across applications and clusters to identify optimization opportunities, forecast future infrastructure needs, and help teams balance performance, reliability, and cloud spend.
Ask Atlas in plain English get a forecast built for your exact traffic pattern
Breaks down CPU, memory, and cost impact of a 50x festive season surge
Generates a capacity graph showing exactly when current infra will fall short
Approve the recommended config changes system ready before the spike arrives
Automated Remediation with Atlas
Atlas transforms incident insights into actionable remediation plans by recommending fixes, validating recovery paths, and reducing mean time to resolution.
Atlas detects an error rate spike in checkout, triggers RCA automatically
Identifies a faulty deployment as the root cause, rolls back to previous version instantly
Validates recovery confirms error rates back within SLA, system at baseline
Full incident timeline attached from problem, fix, and proof, no one paged
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Frequently Asked Questions
How does Agent SRE achieve 70% autonomous incident resolution without compromising safety?
Agent SRE uses pre-approved, battle-tested runbooks combined with intelligent safety guardrails and comprehensive audit trails. Every automated action is logged and follows established procedures that have been validated by your team. The system maintains strict boundaries around what actions it can take autonomously, escalating to human operators when situations fall outside its approved parameters or require judgment calls that exceed its confidence thresholds.
What makes Agent SRE's "Cross-Domain Intelligence" different from traditional monitoring tools?
Unlike traditional tools that operate in silos, Agent SRE understands both application-level patterns and infrastructure behaviors simultaneously. It can correlate a spike in API response times with underlying Kubernetes pod resource constraints, or connect database query patterns to storage I/O bottlenecks. This comprehensive view allows it to identify root causes that span multiple layers of your stack, often uncovering issues that would take human engineers hours to trace across different monitoring systems.
How does the Continuous Learning feature handle team turnover and organizational changes?
Agent SRE builds and maintains institutional knowledge that persists beyond individual team members. As it learns from incidents, it documents system quirks, failure patterns, and resolution strategies in a centralized knowledge base. When engineers leave or teams reorganize, this accumulated wisdom remains accessible and continues to inform future incident response.
Can Agent SRE predict specific types of outages, and how much advance warning does it provide?
Agent SRE's Operational Intelligence Engine identifies subtle performance degradations and anomaly patterns that historically precede major incidents. It can predict issues like resource exhaustion, cascading failures, and performance bottlenecks, typically providing hours to days of lead time depending on the failure mode. The system learns from your specific environment's patterns, becoming increasingly accurate at predicting the types of outages most relevant to your infrastructure and applications.
How does the Natural Language Operations feature work with existing tools and workflows?
Agent SRE acts as a unified interface that can interpret questions in plain English and translate them across your existing monitoring stack. You can ask "Why are checkout API response times spiking?" and it will pull data from application logs, infrastructure metrics, and database performance indicators to provide a comprehensive answer.
