Key Takeaways

  • Traversal builds an AI site reliability engineer that diagnoses system collapses at Fortune 500 enterprises like American Express and PepsiCo.
  • Distributed outages mimic viral epidemics; when one microservice fails, cascading alerts generate thousands of false leads across monitoring stacks.
  • Engineering incidents are shifting from human syntax errors to internal AI chaos, such as unmonitored internal agents overloading production databases.
  • The primary customer base for site reliability tools is pivoting from human on-call engineers to autonomous coding agents checking pull requests before merge.
  • Traversal charges on a usage-based structure tied strictly to the number of active incident investigations.

The Epidemic Problem in Modern Architecture

When a core banking service drops, ten thousand users fail to log in at the same second. Every dashboard turns red at once. Traditional monitoring software flags dozens of failing services, but flagging downstream errors does not identify what broke first. Correlation drowns out causality.

Traversal CEO Anish Agarwal points out that diagnosing modern outages requires untangling cause and effect under extreme noise. “Our whole study was how do you get these systems to think about cause and effect relationships from data,” (play from 1:35:22) Agarwal said. “When something breaks everything breaks. It's like an epidemic that's flowing through your system, and so finding root cause is like finding a needle in a hay stack with fake needles everywhere.” (play from 1:35:31)

A distributed stack spreads errors horizontally across microservices, message queues, and cloud datastores. A single database lock can trigger timeout alerts across fifty unrelated internal applications. Human incident commanders waste hours chasing symptoms because their monitoring tools report where the damage appeared, not where the infection started. Causal machine learning builds a directed graph of the event, filtering out the fake needles to isolate the originating fault.

Rogue Agents and the Internal Heart Attack

The cause of enterprise downtime is also shifting. For two decades, outages came from predictable human mistakes: bad configuration pushes, unindexed database queries, or unannounced traffic spikes. Today, the rapid spread of internal autonomous agents creates unpredictable system loads.

A developer spins up a local workflow to scrape internal data. An autonomous pipeline initiates thousands of concurrent writes without rate-limiting. Suddenly, production latency spikes and the security team assumes the company is under external attack.

“Every incident you have feels like a real heart attack to the entire company,” (play from 1:37:59) Agarwal explained. “You don't know: did I cause it? Am I getting breached by some sort of frontier model that is acting like the way they shouldn't act? Is it some agents that someone has deployed within the company and they're over-pumping my database?” (play from 1:38:04)

Engineers now spend their on-call rotations debugging actions taken by automated tools rather than actions taken by human teammates. When automation causes the issue, human triage speed fails to keep pace.

Software Built for Non-Human Users

Because the volume and speed of software updates now outstrips human review capacity, the end users of diagnostic tools are changing. Traversal sells access through a usage-based model based on the number of investigations run. Increasingly, the software buying and querying that access is another bot.

“Actually the biggest users of the product now are becoming the coding agents themselves,” (play from 1:41:24) Agarwal noted. “They call Traversal all the time to figure out: hey, if I deploy this, is it going to break something? Is it going to cause a security exposure?” (play from 1:41:29)

Instead of waiting for code to hit production and alert a tired engineer at three in the morning, coding agents ping Traversal during the pull request phase. The causal engine evaluates the proposed code against historical failure paths across the enterprise architecture. If the change creates a systemic vulnerability, the agent receives an immediate block before merge. Software maintenance is becoming a closed loop between agents that write code and agents that protect uptime.

What to Do With This

Audit your internal agent permissions this week. Identify every automated agent or script running in your repositories with direct write access to staging or production environments, and place them behind strict rate limits. Before letting autonomous agents open and merge pull requests, enforce a pipeline check that maps dependency blast radius rather than relying solely on passing unit tests.