Resolve AI

What if AI could not only solve problems but actively prevent your next major incident? 🔄 Swipe through to uncover 5 benefits that are making engineering teams rethink everything they knew about system reliability - this might just change how you view the future of DevOps.

At the heart of modern software systems lies a complex web of services, dependencies, and interactions that can make incident response feel like searching for a needle in a haystack. That's where Resolve AI's knowledge graph technology comes in, automatically mapping and understanding your entire technical environment with support for over 50,000 nodes and 500,000 edges, enabling our AI to autonomously navigate through system relationships to identify root causes without constant human input. By combining our custom Gragg query language with agentic AI, we're revolutionizing observability - letting machines handle the complexity of modern systems so engineers can focus on building rather than firefighting. Read more about our knowledge graph capabilities here: https://lnkd.in/gNh3HPhu

Imagine having a production engineer who can analyze thousands of data points instantly, and continuously learns from every incident. We're excited to share how AI Production Engineers are revolutionizing incident management by reducing MTTR from hours to minutes while enabling human engineers to focus on strategic improvements instead of firefighting. Learn more about how this technology is transforming the future of production engineering and improving work-life balance for engineering teams in our blog post. https://lnkd.in/gHnBmHFg

On-call is one of the toughest roles in engineering—it’s where the real challenges surface. It’s not just about responding to pages at 3 a.m.; it’s about untangling complex systems under pressure, diagnosing root causes with limited data, and collaborating across teams to keep services running smoothly. Every incident brings unique challenges: Unknown unknowns that require deep dives into unfamiliar codebases. High-stakes decisions where delaying a feature or rolling back a deployment isn’t just technical—it’s political. Burnout risk, as you juggle firefighting with long-term projects. On-call engineers are the unsung heroes who keep the internet running, often in the shadows. Let’s give them the recognition they deserve. 🌟 What’s the hardest challenge you’ve faced on-call?

Discover how Resolve AI's knowledge graph is revolutionizing incident response by enabling AI to autonomously navigate complex production environments with up to 50,000 nodes and 500,000 edges. By automatically mapping and understanding the relationships between services, pods, and components, our intelligent technology eliminates the need for manual investigation, allowing engineering teams to focus on building rather than firefighting. https://lnkd.in/gAKcix7k

The rise of efficiency engineering represents a fundamental shift in how we build and scale technology. It's no longer just about moving fast - it's about moving fast within constraints. Here are 5 key takeaways from a new blog post by Nishanth Salinamakki from Meta • The era of infinite infrastructure scaling is over. Success now requires balancing aggressive product innovation with strict resource constraints. • Great efficiency engineers are part detective, part diplomat. Beyond the technical skills, you need to build consensus across teams when making tough calls like delaying feature launches or rolling back experiments that cause performance regressions. • Small inefficiencies compound dangerously. One Instagram incident stemmed from dozens of tiny A/B tests that individually seemed fine but collectively pushed system usage to 110% of capacity. The fix required both immediate actions (pausing tests) and long-term solutions (better monitoring). • Tools matter, but interpretation matters more. Modern efficiency engineering uses sophisticated monitoring dashboards and profiling tools. However, the critical skill is being able to analyze this data effectively to identify real problems vs normal variations. • The future is AI-augmented efficiency engineering. While human judgment remains essential, AI systems are becoming invaluable for connecting patterns across diverse data sources, streamlining root cause analysis, and surfacing the most relevant information for decision-making.

Kubernetes promises effortless scaling, but 2AM alerts tell a different story. Learn how Resolve AI's autonomous agent transforms K8s troubleshooting from guesswork into guided resolution. Our AI Production Engineer provides 24/7 expertise, mapping your environment through knowledge graphs and delivering noise-free analysis across all telemetry. https://lnkd.in/gsMcYWxT

On-call can be challenging, even for the most sophisticated teams. With roles like efficiency on-call emerging, the complexity is increasing—but agentic AI might just be the way forward. You can read more about efficiency engineering in the this post by Nishanth Salinamakki from Meta https://lnkd.in/g8PDf9nU

Ever wonder how AI can transform incident response? We're excited to share how Resolve AI's knowledge graph technology is revolutionizing system observability. Our latest blog post dives into how we're empowering AI to autonomously navigate complex production environments, helping teams shift from manual investigation to intelligent, automated incident resolution. Discover how we're bringing a new paradigm to observability: https://lnkd.in/gAKcix7k

Ever wonder how AI can transform incident response? We're excited to share how Resolve AI's knowledge graph technology is revolutionizing system observability. Our latest blog post dives into how we're empowering AI to autonomously navigate complex production environments, helping teams shift from manual investigation to intelligent, automated incident resolution. Discover how we're bringing a new paradigm to observability: https://lnkd.in/gAKcix7k