Company Introduction and Vision

• Traversal co-founders Anish and Raj building AI agents for DevOps and SRE transformation
• Companies currently have "war rooms and armies" of DevOps/SRE engineers troubleshooting production failures
• Production downtime costly, sometimes life-or-death situations
• Problem worsening with AI-generated code and "vibe coding"
• Traversal believes agents are scalable solution, seeing exciting results from deployed troubleshooter agents

Healthcare Analogy for DevOps/SRE Hierarchy

• Stage 1: Heart attack (high severity incidents) - solve immediately, nothing else matters
• Stage 2: Chronic conditions (daily streams of alerts, deployment safety checks)
• Stage 3: Life optimization (5-year infrastructure planning, strategic investments)
• Current reality: DevOps/SRE teams having "heart attack twice a week" plus chronic daily issues
• Traversal's goal: AI handles stages 1-2, humans focus on creative strategic work (stage 3)

Impact of AI-Powered Coding Tools

• Short-term: Tale of two worlds
  • Fast fashion coding: disposable code, reliability doesn't matter
  • Mission-critical systems: payments, financial institutions, security, streaming
• AI tools pass local unit tests but break at scale when systems interact unexpectedly
• Major issue: hard to debug AI-generated code due to lack of context
• Risk: Either ban AI tools due to downtime or need new maintenance tools

Root Cause Analysis (RCA) Lifecycle

• Customer reports issue → escalates through 5 engineering organization ladders
• DevOps/SRE team determines incident severity/impact
• Creates Slack incident channel with 30-50 people
• Implicit blame game, "who done it" atmosphere
• 10x engineer emerges as "Sherlock Holmes," figures out root cause
• Rollback/hotfix implemented, longer-term fix planned later
• Observability tools are #2 software spend after cloud, yet RCA remains manual

Observability Limitations

• Current tools: MELT data (metrics, events, logs, traces)
• Focus on data creation, storage, visualization
• Complex troubleshooting workflow remains manual
• Fragmented landscape: DataDog, Splunk, Dynatrace, Elastic, Grafana, ServiceNow
• Pricing models based on data storage, no incentive for cross-platform insights
• AI agents can automate complex workflows beyond storage/visualization

Traversal Product Architecture

• Multi-agent system with LLM orchestration of tools
• Tools: data fetching (logs, metrics), data processing, statistical analysis (anomaly detection)
• Sequential approach needed - single trace may not fit LLM context
• Gradually builds context to reach root cause
• Mirrors human troubleshooting but more systematic, following Google SRE handbook principles
• Less reliant on system knowledge gaps, more comprehensive coverage

Performance and Conditions

• Works better at large enterprises vs Series A startups
• Large enterprises: mature observability, fragmented teams, rich data available
• Success depends on reasoning steps being findable in data
• When root cause in data: L4 autonomous level, >90% accuracy, 2-4 minutes resolution
• When root cause not in data: L2 level, finds important symptoms to aid human debugging
• Gold standard evaluation: live incidents (2-3 per week), postmortem comparison

Technical Implementation

• Offline phase: learns dependency maps using LLMs and statistics
  • LLMs understand semantic relationships between logs/tags
  • Statistics extract causal relationships from time series variations
  • Self-play prioritizes promising RCA paths
• Online phase: uses real-time info + dependency map for incident investigation
• Setup time: 5-10 hours (small customers), up to 1 day (large customers)
• Read-only data access requirement

Technology Stack and Constraints

• Must use customer's existing LLM contracts (OpenAI, Anthropic)
• Custom models create "security hell for about a year"
• Fine-tuning within customer environment using incident feedback
• Architecture designed for reasoning model improvements (September bet paid off)
• Constant 6-month technology bets required

Academic Research Translation

• Raj's PhD: gene regulatory networks, CRISPR interventions at Broad Institute
• Techniques for learning causal structure between genes
• Direct application: genes → microservices, interventions → PR changes/system breaks
• "Sheer luck" that grad school research applied perfectly

Team Composition and Culture

• 90% engineers, mix of ML PhDs and traditional software engineers
• Infrastructure engineers for scaling (thousands of network calls per investigation)
• Product managers joining
• Unified by excitement for generative AI, willingness to adapt/discard
• Experimental mindset more important than credentials
• Quick iteration on AI/data hypotheses

Future of Observability Teams

• Will need fluency in both traditional systems and AI system reliability
• AI systems break in unique ways requiring new approaches
• Logging will fundamentally change - optimized for AI consumption vs human reading
• More information in message fields, less concern about human readability
• Connection between engineering systems and business logic remains challenging

Vibes Coding Future

• Mission-critical systems will use AI tools but with better testing
• Focus shifts from "how code was written" to "does it fulfill function"
• Increased system interaction failures due to third-party SLA misses
• Harder debugging without code context knowledge
• Traversal's role becomes more critical for subtle interaction issues

Rapid Fire Responses

• Next 12 months breakout: reasoning model applications, diagnostics, healthcare
• Admired founder: Doina Precup (reinforcement learning researcher)
• Recommended reading: "The Bitter Lesson" by Rich Sutton
• Admired companies: Glean, Perplexity
• Personal AI tools: ChatGPT exclusively
• 5-year prediction: vibes coding in banking/healthcare "to some extent, yes"