How Observability and Telemetry Can Enhance the Practice of Software Engineering

2026-04-23 · Source: InfoQ · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Martin Thwaites, in a GOTO Copenhagen talk, emphasized that observability must evolve to support modern serverless, event-driven, and cell-based architectures. He highlighted OpenTelemetry as a vendor-agnostic tool that decouples telemetry from specific solutions, enabling developers to produce consistent, high-quality data describing system behavior in production. This approach facilitates faster debugging, improves reliability, and boosts developer productivity. Thwaites also introduced Weaver, a tool that helps define shared telemetry vocabularies beyond standard attributes, ensuring consistency and enabling live checking against approved conventions. He argued that curating telemetry is a development task, not an operations task, and is crucial for improving metrics like Mean Time To Resolution (MTTR) and Mean Time To Detect (MTTD), as well as developer satisfaction and defect rates. Observability is particularly vital for AI applications, allowing teams to ask unforeseen questions about system reactions to dynamic inputs.

Key takeaway

For AI Architects and CTOs building modern, distributed systems, prioritizing robust telemetry and observability is critical. Your teams should treat telemetry as a core development task, not an operational afterthought, to ensure systems are observable from inception. This approach will significantly reduce debugging time, enhance system reliability, and provide the necessary insights to understand and manage the unpredictable behaviors of AI-driven applications.

Key insights

Modern observability, driven by OpenTelemetry, is crucial for understanding complex, distributed systems and AI applications.

Principles

• Telemetry is a development task.
• Consistency in telemetry is vital.
• Decouple telemetry from vendors.

Method

Use OpenTelemetry to emit consistent, high-quality telemetry data. Employ tools like Weaver to define shared vocabularies and enforce conventions, integrating telemetry into a test-driven development workflow.

In practice

• Adopt OpenTelemetry for vendor-neutral data.
• Define shared telemetry vocabularies.
• Integrate telemetry into TDD.

Topics

• Observability
• OpenTelemetry
• Serverless Architectures
• Event-Driven Architectures
• Telemetry Consistency

Code references

• open-telemetry/weaver

Best for: AI Architect, CTO, VP of Engineering/Data, Software Engineer, DevOps Engineer, MLOps Engineer

Related on AIssential

• OpenTelemetry Project Publishes “Demystifying OpenTelemetry” Guide to Broaden Observability Adoption — OpenTelemetry provides vendor-neutral instrumentation and data collection, requiring separate backend systems for full observability.
• When Correct Systems Produce the Wrong Outcomes — Autonomous AI systems can drift from intent even when all components function correctly, challenging traditional notions of system reliability.
• Observability and human intuition in an AI world — Observability platforms and AI-driven incident resolution tools enhance debugging and operational efficiency.
• Self-managed observability: Running agentic AI inside your boundary — Self-managed AI deployments necessitate robust internal observability for operational accountability and system reliability.
• Monitoring reliably at scale — Reliable observability requires isolating its components from the systems it monitors to prevent circular dependencies and ensure visibility during outages.
• LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, Arize — LLM application development demands structured observability, evaluation, and automated experimentation to manage non-deterministic behavior and ensure continuous improvement.

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.