InfoQ Homepage Observability Content on InfoQ
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Session Traces and Cost Controls Help Diagnose AI Agent Failures
Session traces and cost controls are emerging as key observability techniques for diagnosing AI agent failures, helping teams spot tool-call loops and runaway spend while preserving enough execution context for post-incident debugging.
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Microsoft Moves AI Governance from Policy to Runtime Enforcement
Microsoft has outlined an AI governance architecture spanning nine governance domains and four functions: policy, control, visibility, and proof. The approach connects policies with runtime enforcement, continuous evaluation, observability, identity, security, and audit evidence to help organizations verify governance requirements as AI applications and agents operate in production.
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How Code in the Age of Artificial Intelligence Becomes Write-Only and Disposable
Artificial intelligence (AI) makes all code write-only,. It’s too dense to read, and tests define the behaviour and become the documentation. Code is also disposable; it becomes easier to rewrite than to debug. Humans can't review AI-generated code at scale. Intent decouples from implementation; developers should focus on creativity.
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Grafana's gcx and MCP Server Reach GA for Telemetry-Driven Agent Development
Grafana Labs has announced general availability for two tools that let AI coding agents query live observability data during development: the gcx CLI and the Grafana MCP server. Both allow agents to pull metrics, logs, traces, SLOs, and Synthetic Monitoring results from Grafana Cloud or a self-hosted stack
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Cloudflare Adds Agent Tracing, with Truncation Limits and Uneven Payload Defaults
Cloudflare launched agent tracing, adding spans for agent invocations, model calls, tool runs, and approvals to existing Workers traces. Sessions replay turn by turn, though the docs warn traces are not lossless and payloads may be truncated. Payload recording defaults differ by framework, and from October 1, 2026 every span counts as a billable event.
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More Incidents Don't Necessarily Mean Less Reliability
One of the most common assumptions in engineering leadership is that a rising number of reported incidents signals declining system reliability. However, a recent article from Great Circle argues that the opposite is often true: an increase in incident counts may actually indicate that an organization's incident management culture is improving.
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Instacart Builds Blueberry, an AI-Powered Assistant to Help On-Call Engineers Investigate Incidents
Instacart introduced Blueberry, an AI-assisted incident response system that helps on-call engineers investigate production issues faster. It combines AI agents, operational data, and historical incident knowledge to generate grounded root cause hypotheses in Slack. It uses parallel subagents, MCP integrations, and incident history to reduce investigation time while keeping engineers in control.
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AI Is Transforming Incident Response - But the Hardest Problems May Still Belong to Humans
Artificial intelligence is rapidly changing how engineering teams respond to production incidents, offering the ability to summarize incident channels, analyze unfamiliar code, suggest remediation steps, generate pull requests, and increasingly assist with diagnosis.
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HubSpot Redesigns JITA Authorization with Rule Engine Architecture
HubSpot has redesigned its Just-In-Time Access (JITA) authorization system using a rule engine architecture. The system evaluates access requests through independent rules organized as a directed acyclic graph, adding structured decision metadata, rule-level observability, and governance workflows to replace complex conditional authorization logic.
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AI Root Cause Analysis Shifts from Model Reasoning to Context Engineering
Engineers are increasingly arguing that modern LLMs can already reason through root cause analysis once given correctly prepared context, shifting the hard problem to the pipelines that correlate telemetry. A Coroot experiment across eleven models offers early evidence for the claim.
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Expedia Uses AI-Driven Service Telemetry Analyzer to Accelerate Incident Investigation
Expedia Group has introduced STAR, an internal AI-assisted observability platform that helps engineers investigate production incidents using service telemetry and LLMs. Built with FastAPI, Datadog, Celery, Redis, and Langfuse, STAR follows structured workflows to analyze telemetry, generate root cause assessments, and support incident response while keeping engineers in the loop.
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QCon AI New York 2026: Registration Opens for December 15-16 Production-AI Conference
QCon AI New York 2026 (Dec 15-16) has opened registration at The Westin Jersey City Newport. Six tracks on production AI, chaired by Eder Ignatowicz with Faye Zhang and Wes Reisz. First sessions announced in August, full program by November.
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Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation
Stripe introduces a benchmark suite to evaluate whether AI agents can build real-world Stripe integrations across backend, frontend, and browser-based checkout workflows. The study examines end-to-end software engineering capability, focusing on execution, testing, and validation gaps in agentic systems under production-like constraints.
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OpenTelemetry Graduates to CNCF's Highest Maturity Level
The Cloud Native Computing Foundation (CNCF) has announced the graduation of OpenTelemetry, elevating the project to the foundation's highest level of maturity and formally recognizing it as production-ready for enterprise use.
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Instacart Scales Personalized Marketing via Configuration-Driven Multi-Tenant Platform
Instacart redesigned its personalized marketing system using a configuration-driven multi-tenant architecture on Storefront Pro. The system replaces retailer-specific implementations with a shared execution engine, enabling scalable personalization, faster configuration propagation in under a minute, and 99.9% delivery success across hundreds of retail banners through a unified campaign platform.