Unlocking AI: Auto-Documentation & Debugging for Distributed Systems
AI is everywhere. Depending on who you ask, it’s either making developers obsolete, or it’s just hype. But for those of us who’ve actually used AI tools in real-world engineering workflows, especially in complex distributed systems, the truth lies somewhere in between.
At Multiplayer, we’ve spent the past few years exploring how AI can—and can’t—help solve two of the most persistent challenges in distributed systems: documentation and debugging.
This post captures our biggest lessons, pain points, and what we’ve learned about building AI-powered tools that actually work for engineers.
Why Documentation and Debugging Still Hurt
I’ve worked in distributed systems for decades, across Citrix, startups, and scaleups. And the two pain points that have followed me at every stage? Documentation and debugging.
Let’s start with documentation. And I don’t mean code comments. I mean the entire constellation of architectural knowledge: API specs, design decisions, integration guides, tribal knowledge buried in Confluence pages or Slack threads. And yes, the infamous whiteboard snapshot titled “final system diagram.”
Over the years, I’ve seen every well-meaning attempt to keep documentation up to date:
- Embedding tech writers into the team (who ended up chasing engineers on Slack)
- Adding “write docs” as a sprint task (a permanent resident of the backlog)
- Rotating documentation duties (which led to sync meetings where no one knew the full picture)
The truth is: We’re trying to document living, evolving systems with static tools. It doesn’t scale. It doesn’t stay accurate. And it definitely doesn’t help during production incidents.
My experiences with debugging are equally painful, and it’s deeply connected with documentation. Because when docs are out of date (or just missing), understanding the system becomes an archaeological dig.
But debugging adds its own special flavor of chaos:
- You're handed a vague bug report with zero reproduction steps.
- You open six tabs: one for screen replays, one for logs, one for traces, one for dependencies, and three more just because you’re hoping they might have something useful.
- You waste hours grepping through logs files.
And all this, just to understand the problem, before you’ve even started to fix it.
These are not a fringe problems. They are daily ones. Across every team, at every stage of growth.
And the cost isn’t just time. It’s also confidence. Engineers grow afraid to touch parts of the system. Knowledge gets siloed. Tech debt quietly builds.
Where AI Can (and Can’t) Help Documentation
We started by asking: where does AI actually add value? Today, most AI tools help with things like auto-commenting code, generating OpenAPI docs, or creating architecture diagrams. That makes sense: AI is great at structured, repetitive tasks with clear inputs and outputs.
Think of it like a junior intern: fast, enthusiastic, and helpful… if your instructions are specific. But give it something ambiguous, and things fall apart quickly.
So where does AI fall short in documentation?
1. It’s not thorough
Good documentation doesn’t just say what the system does. It explains why it was built that way. The trade-offs you made. The assumptions you had. The risks you accepted. That knowledge lives in design docs, Slack threads, and meetings—not in code. AI can’t invent that context.
2. It's not trustworthy
AI can’t verify its own output. Even if you avoid outright hallucinations, the quality of the output is still directly tied to the quality of the input. And here’s what most of us are dealing with in the real world:
- Legacy systems with spaghetti code
- Siloed teams duplicating functionality
- Missing or inconsistent documentation
Even if your code is pristine, can you confidently say you have a single, accurate source of truth for your system architecture? Most orgs can’t.
3. It’s not current
Even if you get a decent output, it’s just a snapshot in time. As soon as someone merges a PR, your AI-generated docs are already stale. Without real-time awareness and context—API examples, environment variables, linked design decisions—it’s just another thing to maintain manually.
What’s Actually Needed in Documentation
If we want documentation that’s useful, up-to-date, and reflective of how systems actually work, we have to stop treating it like a one-time deliverable. It has to be a living capability.
Here’s what we’ve found to be essential:
1. Real-Time Visibility
Documentation must be grounded in reality. That means live architecture maps, service dependencies, request/response examples, data flows, and API endpoints.
The good news? Your system already emits this data via telemetry. The challenge is connecting and visualizing it in human-readable ways.
2. Context Around Design Decisions
Visibility is great, but useless if you don’t know why a component exists. Documentation should include architectural trade-offs, deferred decisions, known limitations, and links to relevant tickets and ADRs. That way, future engineers (or future you) can make changes without guesswork.
3. Collaboration
Docs can’t live in silos. Everyone contributes: architects log decisions, devs update components, QA flags issues, support teams share user pain points. When documentation is treated like a shared workspace, it becomes an actual asset.
How We Built It at Multiplayer
We didn’t start with AI. We started with visibility.
- First, we structured and surfaced telemetry data to create a live source of truth.
- Then, we built auto-generated architecture diagrams—always in sync.
- Next, we added collaborative Notebooks, so teams could capture the “why” behind system behavior.
Once that foundation was in place, then we layered in AI: assistants that can answer questions, generate integration tests, run code, and operate through interfaces like IDEs and chat tools—backed by a system that actually understands your architecture.
Otherwise, you’re just generating stale content faster.
Debugging with AI: More Than Summarization
Modern AI-powered debugging tools can do some amazing things:
- Search huge logs
- Summarize incidents
- Surface anomalies
- Suggest root causes
That’s a huge win over sifting through five different tabs. But again, the limitation is context. AI alone can’t:
- Correlate frontend bugs with backend traces
- Understand how a change in one service cascades across others
- Detect architecture drift over time
That’s why we built Multiplayer’s Platform Debugger.
It provides deep session replays—from frontend interactions to backend traces, request/response data, and service dependencies—all in one place.
Once we had that visibility into bugs, we could connect directly with tools like Cursor to send a bug report and ask it to suggest and implement a fix.
See here a sneak peak of how that would look like:
Final Thoughts
AI can help, sometimes dramatically. But AI alone isn’t enough. It doesn’t understand your systems, your history, or your edge cases.
To really move the needle in documentation and debugging, you need:
- Real-time system visibility
- Embedded design context
- Collaborative, living documentation
- End-to-end session data
At Multiplayer, we’re building tools that do this by design.
Not to replace your existing stack, but to connect the dots between your system’s behavior, architecture, and people. And yes, we use Multiplayer ourselves every day.
Watch the full webinar here:
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