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The boundary of 'what AI can do' is moving. The debugging agent is a useful case study. The first generation of coding agents tried to fit into existing manual debugging workflows, using the same observability data humans relied on. The result was PR slop: fixes generated from sampled, aggregated, and missing data that looked plausible and failed in production. The next generation is being built differently: session-based, full-stack, pre-correlated runtime data, with issues deduplicated before they ever reach the coding agent. That's the workflow being redesigned around what machines actually need, and not retrofitted into what humans used to do.

Partial data frustrates human debuggers. It breaks AI ones. AI agents need full runtime context (unsampled, correlated, complete) to understand what actually went wrong. Especially in distributed systems, where the failure rarely lives where the symptom shows up.

This is what grepping through sampled logs feels like ...

How much of your on-call time is spent figuring out what happened vs. actually fixing it?

Decision fatigue is real in cloud-native development and most of it hits before you write a single line of code. Our Director of Community, @vladistevanovic will be talking about why at @CNDItaly. The part nobody mentions: every bug that shows up resets the whole process. Even wondering 'Where do I even start?' is its own tax on top of everything else.

It's the same problem with low-quality inputs to coding agents. An agent acting on incomplete or noisy data produces worse outputs and erodes confidence in every bug the agent flags. You can't fix that with a better model. You fix it with better data.

If your coding agent introduced a bug today, how long before you'd know?

Our CTO is heading to Minneapolis to say the quiet part loud. 🎤

Your AI coding agent and your observability tools were designed with completely different objectives. That's your debugging gap.

The next production incident is already in your system. The question is whether you'll have the full context when it surfaces.

Quick correction: 🕐 *10:00 AM* PDT | *1:00 PM* EDT | 6:00 PM CET

Our CTO @tomjohnson3 will be joining Jon Haddad, @mmanciop , and Amy Tobey for a panel on agentic observability, hosted by @dash0hq and @TheLeadDev 📅 Tomorrow, WED, 18 MAR 2026 🕘 9:00 AM PST | 12:00 PM EST | 6:00 PM CET

The question they’ll be digging into: now that AI agents can help develop and troubleshoot complex systems, what does it actually take to get there? OTel foundations, data management, guardrails … the unglamorous work that makes the glamorous outcomes possible. Save your spot: leaddev.com/event/a-bluepr…

AI wrote the bug. AI needs to fix the bug. But first it needs to see the whole system and understand the bug.

Same vibe: teams spending hours tuning sampling rates, retention policies, and log filters, instead spending a few minutes setting up full stack session recordings. 👀

The hardest bugs to fix aren't the most complex. They're the bugs invisible to every tool you're *currently* using. 👀

This article examines practical implementation strategies for adopting session recording in development workflows, including: - use cases for root cause analysis - feature development validation - AI-enhanced debugging multiplayer.app/session-record…

Modern session recording tools have evolved beyond user analytics to become essential debugging and development platforms that capture complete request/response payloads, distributed traces, and frontend-backend correlations.

The teams moving fastest right now don’t have “better observability”, they are entirely rethinking their approach to system visibility.

AI agents need the complete execution context or they're just guessing. ✔️ Full user session ✔️ Full backend execution ✔️ Full external API exchanges ✔️ Already correlated

Observability was built on a flawed assumption: collect everything, sample aggressively, hope you caught the right 1%. But this approach breaks completely when an AI agent is the one doing the debugging.