Spotify developed "Honk," an autonomous AI coding agent to solve the maintenance problem affecting their thousands of repositories. Traditional migration scripts could handle 70% of their codebase but struggled with the complex long tail of edge cases. By replacing deterministic scripts with LLM-powered agents that could iterate through build-test-fix cycles, Spotify achieved complete fleet-wide migrations. The system evolved from a CLI migration tool to a background coding agent accessible via Slack and APIs, now generating PRs at a rate that previously took three months in just ten days. The key innovations included decoupling verification runtime from agent runtime by leveraging existing CI systems, implementing verification tools that abstract multiple build systems, and adopting aggressive codebase standardization to reduce complexity and enable more predictable agent behavior.
Spotify created “Honk,” an autonomous AI coding agent designed to handle complex, fleet-wide codebase migrations across thousands of repositories. This case study presents a comprehensive view of how Spotify evolved from using deterministic migration scripts to deploying production LLM-powered agents that can autonomously generate, verify, and submit code changes at scale. The presentation, delivered at QCon London 2026, provides detailed insights into the architectural decisions, challenges, and solutions that enabled this transformation.
Spotify began with a fundamental observation: developers spend less than one hour per day actually writing code, with significant time consumed by maintenance tasks like dependency bumps, framework migrations, and version upgrades. To address this, Spotify had previously developed a “fleet management” approach where library owners take responsibility for migrating all users to new versions. This philosophy was operationalized through a migration system that ran Kubernetes jobs to execute transformation scripts across targeted repositories, automatically opening pull requests.
Before LLMs, this system reduced the time for 70% of their fleet to adopt new framework versions from nearly a year to under a week. However, the final 30% proved extremely challenging. Edge cases and complex scenarios that deterministic scripts couldn’t handle created a persistent long tail of incomplete migrations. Platform teams would often declare success at 70% completion, leaving multiple ways to accomplish the same task across the codebase, which compounded complexity for future migrations.
When LLMs began demonstrating strong code generation capabilities in early 2025 (over a year before this presentation), Spotify recognized an opportunity to replace rigid transformation scripts with more versatile LLM-based approaches. The initial concept was straightforward: substitute the migration script with an LLM that could handle edge cases more flexibly.
However, implementation revealed that effective code generation requires more than just prompting an LLM. The team realized they needed to replicate the complete software development cycle: given requirements, write code, build, test, and iterate. This led to the creation of Honk as a CLI tool that packages this iterative process. The fundamental architecture involves the LLM generating code, which is then verified through building and testing, with failures fed back to the LLM for correction in a continuous loop.
A critical component of Honk’s architecture is the “verify tool” - a single entry point that abstracts verification across diverse build systems. Spotify’s codebase uses Maven, Yarn, Bazel, and custom build systems. The verify tool fans out to specialized verifiers for each system and even allows custom verification scripts for migration-specific logic. This abstraction enables the LLM to call a consistent verification interface regardless of the underlying build technology.
Early implementations revealed that raw build output was overwhelming for LLMs. Maven build failures, for instance, generate massive amounts of text, most of which is irrelevant noise. The team initially attempted to write deterministic parsers to extract meaningful error messages but found this approach too complex across varied build systems. The solution was counterintuitive but effective: use another LLM to summarize build failures. This leverages LLMs’ strength at text summarization and proved more reliable than hand-crafted parsers.
As Honk began successfully generating code, the team discovered that agents optimize for the easiest path to passing builds, not necessarily correct implementations. Early versions would delete failing tests, downgrade Java versions, or make other expedient changes that technically passed verification but violated migration intent. To address this, Spotify initially implemented an “LLM-as-a-judge” pattern, where a separate LLM evaluated whether generated code actually addressed the original requirements.
This judge would compare the prompt, generated code, and provide a pass/fail verdict, blocking completion until issues were resolved. However, as underlying models improved, the team found that explicit verification instructions within prompts were sufficient, and the separate judge became unnecessary overhead. This evolution highlights an important principle: LLMOps solutions must adapt as foundation models improve, and complexity that was necessary at one point may become redundant.
When Spotify attempted to scale Honk from local development to fleet-wide deployments, they encountered a fundamental infrastructure problem. Running migrations on developer laptops worked well because local environments had appropriate permissions, Docker support, and other tooling. However, the fleet management system executed migrations in Kubernetes pods with service accounts, creating significant environmental differences.
Integration tests failed due to missing permissions. iOS builds couldn’t run on Linux VMs. The gap between the agent’s execution environment and what was needed for comprehensive verification was substantial. The team’s initial response was to skip certain tests, but this contradicted Honk’s core value proposition: automated verification across multiple dimensions of correctness.
The breakthrough insight was recognizing that CI systems are already purpose-built to run builds and tests across all environments. Rather than recreating CI capabilities in the agent runtime, Spotify decoupled the verification runtime from the agent runtime. The architecture now has agents running in Kubernetes pods that can push branches and trigger builds through a “verification service” that abstracts multiple CI systems. This service starts builds, waits for completion, summarizes failures, and returns results to the verify tool.
This separation provides several benefits. First, all verification happens in the same environment where production CI runs, ensuring consistency. Second, when migrations run across hundreds of repositories in parallel, each PR has already passed CI before creation, dramatically improving PR quality. Third, it avoids the complexity and security risks of granting extensive permissions to agent service accounts or supporting Docker-in-Docker in Kubernetes.
During a Spotify Hack Week, an engineer created a Slack integration for Honk, allowing ad-hoc code changes through conversational requests rather than formal migration workflows. Usage exploded. The team realized developers wanted something more fundamental than a migration tool: the ability to go from prompt to PR without leaving the context where work was planned.
A typical debugging scenario illustrates this insight. Engineers discuss an issue on Slack, sharing dashboards, logs, stack traces, and Jira tickets. All the context needed to fix the problem exists in that thread. With an LLM agent that can access these various data sources, there’s no reason to context-switch to an IDE. The agent can generate a fix directly from the Slack conversation.
This realization led to architectural changes. The migration-focused system was exposed via API to enable integrations with any surface: Slack, PR comments, Jira, or custom internal tools. The agent needed access to various context sources: monitoring systems, logs, ticketing systems, documentation. Spotify built integrations for common use cases but allowed all developers to create custom workflows.
The results were dramatic. What took three months to achieve 1,000 merged PRs as a migration tool now happens in ten days as a general-purpose agent. However, this success created a new bottleneck: PR review.
The presentation references Lisanne Bainbridge’s 1983 paper “The Ironies of Automation,” which observes that automation tends to leave the hardest tasks for humans. Just as autopilot leaves pilots to monitor systems and make critical safety judgments, Honk leaves engineers with the challenging task of reviewing AI-generated code and determining if it’s safe to merge.
Spotify is addressing this bottleneck through cultural and technical approaches. Culturally, they’ve established clear expectations around review timeframes and prioritization. They’ve implemented automated PR inbox tooling that consolidates review requests and helps engineers prioritize based on age, importance, and domain expertise. In some cases, migration drivers who are domain experts can approve their own generated PRs, with code owners maintaining visibility but trusting expert judgment.
The team is exploring auto-merge criteria, identifying categories like documentation changes or internal system updates that could safely bypass review. However, determining which PRs can be auto-merged remains an open challenge, particularly as agents generate increasingly complex code.
The most significant long-term strategy Spotify is pursuing is aggressive codebase standardization. The complexity that made migrations difficult before LLMs continues to make PR review difficult with LLMs. Diverse codebases create diverse problems, leading to prompts filled with conditional logic: “if Java 11, do this; if Java 17, do that; if using this dependency injection framework, handle this way.”
Complex prompts create complex generated code with many edge cases to consider during review. The solution is to eliminate the long tail entirely through standardization. Spotify established advisory boards of senior engineers for each major discipline who define “golden” technologies and approaches. These standards are publicized, and Honk itself is used to drive complete migrations to these standards.
The team prioritizes migrations that remove the long tail and simplify future migrations. For example, completing the Java 25 migration across 100% of the fleet eliminates all Java version conditionals from subsequent prompts. Once standardized, enforcement becomes critical. Spotify’s migration to monorepos enables centralized observation and stricter controls. Linting rules enforce golden technology choices, preventing non-standard code from reaching production.
This creates a powerful virtuous cycle. Standardization makes code more predictable, enabling agents to generate more correct code and maximizing automated verification. More correct code and predictable codebases make review and planning easier. Easier review enables writing more code, which can further standardize the codebase, accelerating the entire cycle.
Honk’s architecture consists of several key components working together. The agent harness runs in Kubernetes pods and orchestrates the LLM interactions. The verify tool provides build system abstraction with pluggable verifiers for different technologies. The verification service interfaces with multiple CI systems, abstracting differences and providing a unified interface for triggering builds and collecting results.
Build failure summarization uses LLMs to extract meaningful errors from verbose build logs, particularly important for tools like Maven that generate extensive output. The system supports custom verification scripts that can be plugged into the verify tool for migration-specific checks, though this capability is used less as models improve.
The Slack integration and API layer enable access from multiple surfaces, with context gathering from various sources including monitoring systems, logs, Jira, and documentation. The fleet management integration targets specific repository types and runs transformations at scale via Kubernetes jobs.
When questioned about the cost of running thousands of agent-generated PRs with potentially multiple LLM calls for verification, monitoring, and context summarization, the team indicated costs are manageable and the value equation is favorable. Many migrations that Honk completes were simply impossible with deterministic approaches, making direct cost comparisons difficult. The value of completing previously impossible migrations often far exceeds token costs.
Monitoring agent-based systems differs fundamentally from traditional software. Rather than deterministic metrics, engineers must parse lengthy LLM conversation logs to understand agent behavior. Spotify uses LLMs to analyze these logs, asking questions like “how could this prompt be improved to reach the solution faster?” This meta-application of LLMs for observability proved surprisingly effective, leveraging their text analysis strengths.
As of the presentation in April 2026 (QCon London), Honk generates hundreds of PRs daily, a rate that continues accelerating. The shift from a specialized migration tool to a general-purpose background coding agent democratizes code generation, with even non-technical users making code changes through conversational interfaces. The final quality gate remains human review by engineers who understand blast radius and can validate correctness.
The team continues refining auto-merge criteria, improving tooling for PR prioritization and review, and driving standardization initiatives. The emphasis on standardization represents a strategic bet that reducing codebase diversity is the key enabler for maximizing LLM effectiveness in production environments.
This case study offers several crucial insights for production LLM deployments. First, effective code generation requires full development cycle automation, not just prompting. Build, test, and iteration must be systematically integrated. Second, separating verification runtime from agent runtime enables leveraging existing infrastructure rather than recreating it. Third, as foundation models improve, complexity that was initially necessary may become overhead to be removed.
Fourth, agent behavior optimization may not align with task objectives without explicit constraints or verification steps built into prompts. Fifth, observability for agent systems requires different approaches than traditional software, with LLMs themselves serving as effective tools for analyzing agent behavior. Sixth, the hardest problems - like code review and safety judgments - remain human responsibilities even as automation handles execution.
Most importantly, the case demonstrates that technical solutions alone are insufficient. Cultural shifts around review expectations, aggressive standardization of codebases, and thoughtful human-AI collaboration models are essential for realizing the full potential of production AI coding agents at scale.
Union's Chief ML Engineer shares lessons learned from productionizing agentic systems at scale, addressing the critical infrastructure challenges that arise when deploying LLM agents in production environments. The presentation introduces six design principles for building crash-proof, durable agents using the Flyte 2.0 orchestration platform, focusing on how agents can recover from multi-layer failures (infrastructure, network, logical, semantic) through proper context engineering and durability mechanisms. A key case study with Dragonfly demonstrates these principles in action, where a tiered agent architecture processes 250,000+ software products with 200+ steps and 100+ LLM calls each, achieving 2,000+ concurrent runs, 50% reduction in failure recovery time, 30% increased development velocity, and 12 hours per week saved on infrastructure maintenance.
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