Multiplayer: the debugging agent for developers.
We connect your favorite coding agent to prod to fix application bugs automatically.
Run us locally and eliminate PR slop.
Multiplayer captures the actual runtime context you (and your coding agent) need to understand your system and fix bugs:
โข User interactions and clicks
โข Session metadata
โข Network requests
โข Console messages
โข Error rate metrics and errors per user (not sampled!)
โข Stack traces, spans, and logs (not sampled!)
โข Request/response headers and content from deep within your system
To go from bug identified to bug fixed, agents need runtime data that observability tools weren't designed to provide. This talk explores that ๐ ๐
Minneapolis last week. New York on June 9-10.
I'll be speaking at @DevWeekNYC a talk I've been wanting to give for a while: From Alert to Action: Redesigning Your Observability Stack for Agentic AI.
I'm attending the Cloud Native Computing Foundation (CNCF) Observability Summit today in ๐Minneapolis, Minnesota.
If anyone is interesting in discussing agentic coding, debugging, or just have a coffee, find me and @BkStephJ1 โ๏ธ
Proud to see our Director of Community and DevRel and on stage at Cloud Native Days Italy 2026.
The cognitive cost of too much choice is something we think about a lot at Multiplayer. It's part of why we built our debugging agent to run locally, right next to your coding agent: less context switching, less decision fatigue, more fixing.
Great talk. ๐
Today I had the privilege of delivering a keynote at Cloud-Native Days Italy 2026 and I'm still processing how much fun it was. ๐คฉ
If you're curious about the research behind the talk, here are the slides with all the sources linked: https://t.co/6TqZUENqp7
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.
There's a story economists love to tell about ATMs and bank tellers.
If you're a developer, you've probably heard it cited in relation to AI tools: the technology won't replace you, it will assist you.
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.
Most engineering teams are using AI to write code faster than ever. But they are also shipping bugs with equal speed. Ultimately, the root cause is a data problem.
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.
โก KEYNOTE SPEAKER: @vladistevanovic
Director of Community & DevRel at Multiplayer
10 years working in the trenches with developers at Squarespace, MongoDB, and Prisma
๐ค Keynote talk: "The Hidden Cognitive Cost of Cloud-Native Sprawl"
Check it out: https://t.co/Vp1w6g0MYq
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.
The Law of False Alerts: โAs the rate of erroneous alerts increases, operator reliance, or belief, in subsequent warnings decreases.โ
Too many alerts and people stop reading them. Too many false positives and people stop trusting them.
Hot take for a room full of observability practitioners:
Logs, traces, and metrics were designed for a world where humans wrote and reviewed every line of code. That world is gone.
I'm speaking at @CloudNativeFdn's Observability Summit North America in Minneapolis on May 21โ22.
Our CTO @tomjohnson3 will be joining Jon Haddad, @mmanciop , and Amy Tobey for a panel on agentic observability, hosted by @dash0hq and @TheLeadDev
๐ ย Tomorrow, WED, 18 MAR 2026
๐ย 9:00 AM PST | 12:00 PM EST | 6:00 PM CET