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. 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.

📅 Today, Wed, 18 MAR 2026 🕘 10:00 AM PDT | 1:00 PM EDT | 6:00 PM CET A panel on agentic observability with @tomjohnson3.bsky.social, @rustyrazorblade.com orblade.com, @michele.dash0.com, and @renice.bsky.social Hosted by @dash0.com and @leaddev.com Save your spot: leaddev.com/event/a-blue...

Our CTO #Thomas Johnson will be joining @rustyrazorblade.com, @michele.dash0.com, and @renice.bsky.social for a panel on agentic observability, hosted by @dash0.com and @leaddev.com 📅 Tomorrow, WED, 18 MAR 2026 🕘 9:00 AM PST | 12:00 PM EST | 6:00 PM CET

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. 👀

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.

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.

You can't debug what you can't see. Sounds obvious, but here's what's actually happening: developers are asking AI tools to fix production bugs based on sampled logs, redacted payloads, and traces that stop at the system boundary.

The real question isn't "can you capture external API data?" It's "do you capture it reliably, or does it require manual discipline (that often breaks down)?" With custom logging, you CAN technically capture everything. BUT...

By the time you find out about an intermittent bug, the context is gone (sampled away or never captured)… or is it? 😇 Maybe you just don’t have the right tool to automatically record the full-stack, session-based context when the bug occurs.

Quick question: How do you debug code you shipped three hours ago that was generated by AI, barely reviewed, and has no proper instrumentation?

⚠️ Sampling is a cost-management strategy not a faster path to debugging. Collecting more data won't fix your debugging problem if you're collecting the wrong data.

With Multiplayer, you get: ✅ 100% traces (no missing data or outrageous bills) ✅ Full request/response payloads and headers ✅ Internal AND external API calls ✅ User steps and frontend data (annotatable) ✅ Full-stack data correlated by session (no manual stitching)

"When debugging, novices insert corrective code; experts remove defective code." - Richard Pattis 👆 This is now more true than ever with AI vibe coded slop

Complexity is a choice.

Instead of: Here are logs from 6 tools, figure it out. You get: Here's everything that happened in this user's session, already correlated. Here's how. 🧵

What is spec-driven development? The approach: Write comprehensive specs → detailed technical plans → task breakdowns → then let AI generate code. Sound familiar? We used to call this Big Design Up Front (BDUF). We spent decades running away from it. 🧵

Auto-correlation tools solve the data problem at the collection layer, not the analysis layer. 👀 Let’s explain that: 🧵

Something feels backwards here.

Everyone is racing to give AI agents access to observability data through MCP servers and API integrations. But here's what they're missing: access to incomplete data is still incomplete.

What if those 20 minutes of information gathering took 20 seconds instead? 👀

Dev: Until AI can do debugging I've still got 90+% of my job. Us: *developing an AI debugging agent with full-stack correlated runtime context * 👀

The difference between 'works fine' and 'production is down' is usually smaller than you think.

GitHub Copilot can scaffold your payment integration in 30 seconds. But when customers report "checkout is broken ❌," you're spending 60 minutes manually correlating: • Browser DevTools for frontend data • Backend logs • Stripe's dashboard for API responses • Sentry for error traces ...

"We need better observability" Adds 47 console.log() statements "We need to see API responses" Screenshots Postman for the 10th time "We need request headers" Digs through network tab while user waits OR... you could just use a full stack session recording with automatic request/response capture.

We built Multiplayer because engineering teams deserve to live the “good” pie chart. 👀

We’re taking a hard look at how easy it is to get started with Multiplayer. If you tried full stack session replays and bounced during setup, your perspective would help us a lot. We’re offering a 💰$50 gift card 💰 for a short feedback chat. More info below 👇

Turns out it's a lot easier when you can just... see what happened.

🎁 The best gift you can give your engineering team? Automatic visibility into request/response content AND headers from EVERY internal service call and external dependency. ❌ No more "Can you add more logs?" ✅ Just complete, correlated, end-to-end payloads, captured automatically

Your code may be clean but it can still be wrong. The problem isn't your skills (probably). It's that you’re missing the full context of what happened across your entire stack.

Tried Multiplayer but didn’t finish setup? We want to understand why and we’ll compensate you for your time. Just book the time 👇 that works for you and tell us what we can do better. No sales pitch. Just feedback.

Building an MCP server because "we should support MCP" is like adding an API because "everyone has APIs.” That's why we built our MCP server for devs who already use our full-stack session recordings and need that info into their AI tools.

The only way to fight AI slop is to give your tools full stack context at the start. Or, failing that, at least have it available when you're fixing prod.

We’re building the only tool that provides full-stack session replays out of the box. It includes all the data you wish you could get from your observability and screen recording tools. If you signed up for Multiplayer but didn’t make it through setup, we want to hear why.

67% of developers now spend MORE time debugging AI-generated code than they did before AI tools. We created a problem by moving faster. But AI (powered debugging) itself might be how we solve it. 🧵 👇

Not all "session replay" features are created equal. Mixpanel's frontend session replays are an add-on to a product analytics tool. Multiplayer's are built for engineers asking: “What issue did the end-user experience?” “Why is the system behaving this way?" “How do we fix this bug?”

Me: "Happy New Year!” Pikachu: "Pi-ka Pika Pika!" Yoda: "Joyful greetings, young one!" My app: *500 Internal Server Error* Good thing we have full-stack session recordings to debug this! Happy New Year, from the Multiplayer team. May your errors be caught and your context be complete. 🎉

New year’s resolution: stop debugging for hours, it doesn’t make you a hero. Start fixing bugs in minutes.

AI can only reason with the context you give it. Full-stack, real-time is a game changer.

🔥 Hot take: Session replay without backend visibility is like having a security camera that only films the parking lot. Sure, you can see someone walked into the building. But you have no idea what happened inside.

End-users don’t care if your team is overworked (or on vacation)… they want their issues resolved, fast.

Today’s hardest problem isn’t (just) building APIs, it’s also debugging how they behave in production. API docs have come a long way to help you understand your APIs… but they are still not enough. Here’s how we got here 🧵👇

Take session recording. Pop into AI tool. Get bug fix. Repeat.