Operations | Monitoring | ITSM | DevOps | Cloud

Today’s IT environments span legacy infrastructure, multiple cloud platforms, and edge systems—each producing fragmented data, inconsistent signals, and hidden points of failure. This scale brings opportunity, but also operational strain: fragmented visibility, overwhelming alert noise, and slower time to resolution. With good reason, public and private sector organizations alike are moving beyond basic visibility, demanding hybrid observability that’s context-aware and action-oriented.

Developers lose hours each week to a familiar troubleshooting loop: chase down telemetry across dashboards, decipher vague errors, and juggle alerts to find the signal worth fixing. Production issues, performance regressions, and security vulnerabilities all demand attention, but they often come with little context for taking action.

To efficiently optimize your LLM application before pushing to production, you need a comprehensive testing and evaluation framework. By running experiments, you can optimize prompts, fine-tune temperature and other key parameters, test complex agent architectures, and understand how your application may respond to atypical, complex, or adversarial inputs. However, it can be difficult to manage your experiment runs and aggregate the results for meaningful analysis.

Getting paged pulls engineers away from meaningful work, yet incident response in many organizations remains manual, reactive, and draining. An alert fires and teams scramble to find the root cause, relying on siloed knowledge, incomplete context, and a few on-call experts who are already stretched thin. The rise of AI coding agents has only intensified this challenge: As teams ship code faster with less human oversight, production systems grow increasingly complex and harder to understand.

Last night, over dinner in Canary Wharf, I found myself catching up with a former colleague who is now leading investments at one of the big banks.

Today’s digital businesses are more interconnected than ever. Industry research shows that 74% of organizations now take an “API-first” approach, and the average application is powered by between 26 and 50 APIs. While this accelerates innovation, it also introduces new risks: when an external provider fails, the impact can be immediate and far-reaching.

Most internal AI projects for IT operations next exit pilot. Budgets stretch, priorities shift, key hires fall through, and what started as a strategic initiative turns into a maintenance burden—or worse, shelfware. Not because the teams lacked vision. But because building a production-grade AI agent is an open-ended commitment. It’s not just model tuning or pipeline orchestration. It’s everything: architecture, integrations, testing frameworks, feedback loops, governance, compliance.