CodeWolf AI Raises $1.7M Pre-Seed to Build Reliability AI Agents for Protecting What AI Ships

The first reliability AI agents built to protect what AI ships—catching failures before they reach production

Over 60% of production code is now AI-generated. Zero reliability agents exist to protect what AI ships. Until now.

The Inflection Point: When AI Agents Take Over DevOps

Something fundamental has shifted in software development. Today, AI coding assistants write more production code than humans. GitHub Copilot, Cursor, Claude Code, Devin, and Codex are no longer experiments—they're essential tools generating billions of lines of code.

But here's the terrifying truth: Incident prevention and resolution was already the hardest part of software engineering—requiring your most senior engineers, taking hours of investigation, and costing enterprises millions in downtime. Now? Your team is shipping 10x faster with AI-generated code, but who's debugging it when it breaks at 3am? Engineers are now investigating code they've never seen, written by AI they don't understand—turning what used to be 1-hour fixes by experts into 6-hour war rooms that cost $100K+ per incident. The complexity has exploded while the expertise to handle it is walking out the door.

Enter CodeWolf AI: Four specialized AI agents that catch what AI coding tools miss, preserve every lesson learned, and prevent the cascade failures that AI-generated code often creates. Today, we're announcing our $1.7M pre-seed funding to solve the most critical problem in modern software: When AI writes the code, who guards production?

From Chaos to Pack Intelligence

Led by Bowery Capital and Ripple Ventures, our funding round includes an exceptional group of angel investors who understand the magnitude of this shift: David Cramer (Founder of Sentry), Jude Gomila (Founder & Unicorn Investor), David Siegel (Founder & CEO of GlideApps), Amanda Robson (Founder of MTF VC), Sachi Shah (Kleiner Perkins Scout), the founders of Drata via Wildcard Capital, Eva Sasson (Developer Experience at Square), Delly Tamer (Early Netflix & Founder of Biztera), Bowen Dwelle (Exited Founder & Writer), Sourabh Bajaj (Co-Founder & CTO of Uplimit), Frank Kuehnel (Ex-Google CTO), Mark McCubbin & Jazzlyn O'Reilly (Co-Founders of SuperDuperSecret), and Ali Uygar Kucukemre (CTO at Reaven Tech).

"The traditional approach to reliability is fundamentally reactive—you wait for things to break, then scramble to fix them," says founder Aykut Gedik, who previously built critical infrastructure at Crunchbase, Talkspace, and The Home Depot. "But the real killer with AI-generated code? It can look absolutely perfect—clean syntax, proper error handling, 100% test coverage, passes every linter, gets approved in code review. Yet it will still cause catastrophic failures because AI doesn't understand YOUR specific infrastructure. Copilot doesn't know your Redis connection limits. Cursor doesn't realize your database can't handle that retry pattern during failover. Claude Code implements caching without understanding your invalidation delays. The most dangerous code today passes every check but fails in production because AI lacks the context of your system's specific constraints, patterns, and dependencies. That's why we need AI agents that understand not just code, but YOUR production environment."

The Revolutionary Four-Agent Architecture

Unlike single-agent solutions that provide fragmented insights, CodeWolf deploys a coordinated pack of four specialized AI agents that work in perfect harmony:

Elder: The Context Intelligence

Elder is the pack's memory and wisdom. It preserves every lesson learned, every incident resolution, every pattern discovered. When your senior engineer who fixed that tricky race condition leaves, their knowledge lives on in Elder. It instantly recalls who fixed similar issues before, what solutions worked, and what patterns to watch for.

Scout: The First Line of Defense

Scout prevents disasters before they happen. Consider this: Copilot generates retry logic that looks perfect—clean code, proper error handling, even exponential backoff. But Scout analyzes it in YOUR context: "This retry pattern will cause a thundering herd effect when your Redis cluster fails over." The AI doesn't know your Redis connection limits. Scout does.

That innocent-looking cache TTL change from 5 to 50 seconds? Scout knows this pattern previously caused memory leaks in your specific infrastructure. Scout catches the deployments that pass every linter, every test, every code review, but would still cause outages during Black Friday traffic.

Hunter: The Root Cause Analyst

When alerts fire, Hunter investigates incidents in real-time. Within seconds, Hunter correlates spikes in database connections with recent deployments, traces issues to dependency updates, and identifies the exact code changes causing problems—all while posting real-time findings in your Slack channel. What takes teams 2-6 hours, Hunter solves in under 2 minutes.

Guardian: The 24/7 Anomaly Detector

Guardian catches the disasters that happen slowly, then suddenly. While you're sleeping, Guardian notices patterns invisible on dashboards—memory fragmentation building toward OOM kills, 99th percentile latency creeping up after batch jobs, that subtle API response time degradation suggesting a broken mobile app release.

Guardian learns YOUR system's rhythm: Monday traffic spikes, Thursday deploys, month-end billing jobs. It knows that when service A slows down by 10ms AND service B's memory increases by 5%, you're 3 hours away from an outage. Your morning Slack briefing isn't about what broke—it's about what Guardian prevented overnight.

Together, they enable teams to ship without fear.

Early Validation: Design Partners Already Preventing Millions in Incidents

We're not building in isolation. CodeWolf is already protecting production for early design partners ranging from YC startups shipping daily to Fortune 500 enterprises managing thousands of microservices. These teams report:

• 87% reduction in incident frequency - Scout catches breaking changes before they deploy
• 90% faster mean time to resolution - Hunter identifies root causes in minutes, not hours
• $2M+ saved in prevented outages - Guardian spots patterns humans miss
• Zero knowledge loss from departing engineers - Elder preserves every lesson learned

"CodeWolf caught a Redis configuration issue that would have taken down our entire payment system during peak traffic," reports one design partner CTO. "Our previous incident with similar symptoms took 6 hours and 8 engineers to resolve. CodeWolf identified it in pre-production in under 30 seconds."

Another enterprise partner eliminated their entire on-call rotation after CodeWolf prevented every critical incident for 3 consecutive months. Their engineers now focus on building features instead of fighting fires.

The AI Production Engineer: Your Dashboardless Observability

Beyond the agent pack, CodeWolf introduces the industry's first dashboardless, next-gen observability solution. Replace your 47 Datadog dashboards with one conversation:

• "Why is checkout slow for European users?"
• "What's our most expensive API call?"
• "Which customer is causing the memory spikes?"
• "Will we hit rate limits during Black Friday?"

Instead of spending 2 hours clicking through dashboards, get instant answers with full context and actionable insights. Complex distributed systems become as easy to understand as talking to a colleague.

The Secret Sauce: Our Internal AI Infrastructure

What makes CodeWolf agents reliable enough for production environments? Our proprietary internal tooling:

Continuous Evaluation Pipeline

Our agents undergo continuous evaluation against thousands of real-world scenarios. Every response is measured, scored, and used to improve the models through reinforcement learning.

Custom Simulation Environments

We create digital twins of our customers' tech stacks, allowing agents to train on their specific architectures, failure modes, and incident patterns. This isn't generic AI—it's AI that understands YOUR production environment.

Reinforcement Learning from Production Feedback

Every resolved incident, every successful root cause analysis, every prevented outage feeds back into our RL pipeline, making the agents smarter with each interaction.

Multi-Agent Coordination Protocol

Our agents don't work in silos. They communicate through a sophisticated coordination protocol, sharing context and collaborating on complex issues that require multiple perspectives.

The Critical Difference: Why Coding Assistants Can't Protect Production

Here's what every CTO needs to understand: Copilot, Cursor, Claude Code, Devin, and Codex are fundamentally different tools built for different purposes.

These coding assistants are primarily built for writing code and building applications. Yes, they can connect to Datadog via MCP for on-demand investigations or review PRs on GitHub. But they're stateless tools that start from scratch with every query.

CodeWolf is different. Our 4-agent architecture builds deep context over months, learning from every incident, deployment, and alert like a seasoned staff engineer—and they do this around the clock, even while you and your coding assistants are sleeping.

Consider this: When your auth service fails every Tuesday after batch jobs, CodeWolf remembers. When deployment #847 introduced a subtle race condition, CodeWolf knows who fixed it and how. Your coding assistant? It starts from zero every single time.

But here's the killer insight: There's a fundamental conflict of interest when using coding assistants for reliability. The same assistant that wrote the broken code or configuration is now investigating its own failures—a concerning pattern for LLMs that may not recognize their own mistakes. It's like asking the arsonist to investigate the fire.

Building the Future from Centers of Excellence

CodeWolf operates from strategic locations designed to access the world's best AI talent: our product and go-to-market teams in San Francisco, and our advanced AI Research Labs consistently working with professors and top talent from ETH Zurich and Bilkent University.

"This multi-hub approach gives us unprecedented access to world-class AI research while maintaining close proximity to our customers," explains Aykut. "Our research collaborations with ETH Zurich—one of the world's leading institutions in AI and computer science—and Bilkent University ensure we're pushing the boundaries of multi-agent systems with the brightest minds in academia, while our SF team ensures we're solving real production problems."

Early Design Partners Are Already Seeing Revolutionary Results

We're working closely with select design partners who are experiencing the future of production reliability today:

• 90% reduction in MTTR because CodeWolf already knows the solution from similar past incidents
• 87% fewer false positive alerts in the first month alone
• $100K+ saved per prevented outage (and most partners prevent one in their first week)
• Zero 3am wake-ups for on-call engineers in the past 30 days

"We're engaging with CodeWolf as a design partner because the potential is transformative," shares the Head of Engineering at a Series B fintech startup. "In just our first week of testing, CodeWolf prevented a database cascade failure that would have taken down our entire platform during peak hours. The system learned from a similar incident we had six months ago and blocked the deployment before it reached production."

Another VP of Engineering at a high-growth SaaS company reports: "What convinced us to partner with CodeWolf wasn't just the technology—it was seeing our senior engineers actually excited about the possibilities. For the first time, we have AI that understands our specific infrastructure, not generic best practices."

With our funding secured, we're rapidly scaling both our agent capabilities and our team. We're actively seeking:

• Growth & GTM Lead (San Francisco)
• Founding Engineers (Global/Remote)
• AI Engineering Interns (Global/Remote)

The Clock Is Ticking

Every day without CodeWolf is another day your competitors ship faster while you debug longer. Another night your best engineers burn out from 3am alerts. Another incident that costs six figures and customer trust.

The math is simple: Within 15 minutes of connecting your tools, CodeWolf prevents its first incident—that's faster than onboarding a new hire to Slack. In your first 24 hours:

• Hour 1: Scout catches breaking changes in your next PR
• Hour 2: Hunter reduces your next incident from 2 hours to 10 minutes
• Day 1: Guardian starts learning your patterns and detecting anomalies
• Day 1: Elder has already indexed your entire incident history for instant recall

By week's end, you've avoided 20+ incidents, reduced MTTR by 90%, and your engineers are sleeping through the night because CodeWolf handled the 3am alerts.

The question isn't whether you need CodeWolf. It's whether you can afford another AI-caused outage that damages customer trust and burns out your team.

Request early access at codewolf.ai or reach out directly at hello@codewolf.ai.

Ship without fear. Let the pack handle the rest.

As Aykut concludes: "We're not building another AI SRE tool or observability platform—we're building the reliability layer for the AI development era. Our agents sit on top of your existing Datadog, Sentry, and CloudWatch, transforming raw signals into prevention and protection. This is essential infrastructure for modern software: When AI writes 60% of your code, you need AI agents that understand it, prevent its failures, and protect your production 24/7. Welcome to the pack."

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CodeWolf AI—Your AI Agent Pack for Production Reliability.

About CodeWolf AI: CodeWolf is pioneering the future of software reliability through coordinated AI agents. Our four-agent architecture—Elder, Scout, Hunter, and Guardian—works in perfect harmony to prevent, detect, and resolve production issues before they impact users. Founded by infrastructure veterans and backed by leading investors, CodeWolf is headquartered in San Francisco with AI Research Labs consistently working with professors and top talent from ETH Zurich and Bilkent University.