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Birgitta Böckeler, Distinguished Engineer at Thoughtworks, returns to discuss the rapid evolution of AI in software delivery. She touches on the evolution from vibe coding, the changing tools landscape and the more autonomous agents that, besides higher velocity, introduce higher risk.
Martin Kleppmann, an associate professor at Cambridge and author of Designing Data-Intensive Applications, discusses the evolution of data systems over the last decade, mainly the shift from monolithic databases to modular building blocks. Kleppmann underlines the importance of moving from cloud-centric data storage systems to decentralised data storage similar to Bluesky’s AT protocol.
OpenAI’s Bonnie Xu discusses Kepler, an internal AI data analyst agent built to query 600+ petabytes of data. She explains how they overcome context window limits using MCP, automated code crawling, and RAG. Xu also shares how their team leverages scoped semantic memory for self-learning and utilizes AST-based LLM grading to build a robust, regression-free evaluation pipeline.
In this podcast, Shane Hastie, Lead Editor for Culture & Methods spoke to Craig McLuckie, co-creator of Kubernetes and CEO of Stacklok, about the impact of AI coding tools on open source communities and engineering teams, designing deliberate organisational culture, and navigating evolving career paths for engineers in the age of AI.
Netflix engineer Casey Bleifer shares how to achieve rapid, automated code changes across a massive, diverse software fleet. She discusses building an event-driven orchestration platform using composable, Lego-like steps, and explains how Netflix utilizes automated canary validation, compliance checks, and a custom "confidence metric" to eliminate the long tail of manual engineering migrations.
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InfoQ Homepage News CircleCI Introduces Chunk Sidecars to Bring CI Validation Directly Into AI Coding Workflows
Jun 19, 2026 3 min read
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CircleCI has launched Chunk Sidecars, a new capability designed to bring CI-style validation directly into an AI coding agent's inner development loop. The feature provides fast, pre-configured cloud environments where AI agents can run tests, linting, formatting, and validation before code is ever committed or pushed to a CI pipeline. CircleCI says the approach addresses one of the biggest emerging challenges in AI-assisted software development: ensuring that code generated at AI speed can be validated just as quickly.
The release marks a significant evolution in how CI/CD platforms are adapting to the rise of agentic development. Traditionally, developers write code locally and rely on CI pipelines to catch problems after commits are pushed. But as AI agents generate code at increasingly high velocity, this feedback cycle can become a bottleneck. CircleCI argues that by the time conventional CI discovers an issue, the AI agent has already moved on, losing valuable context and requiring additional iterations, compute resources, and human intervention. Chunk Sidecars aim to solve this by moving validation earlier into the development process, allowing agents to self-correct before code reaches the pipeline.
At its core, Chunk Sidecars are lightweight, reproducible cloud environments that mirror important aspects of a project's CI pipeline. Developers or AI agents can configure these environments once, snapshot them with dependencies and tooling pre-installed, and reuse them across sessions. As an AI agent writes code, validation hooks automatically run tests and quality checks inside the sidecar environment whenever the agent reaches a stopping point.
This creates what CircleCI calls an "inner-loop validation" process, where AI agents receive CI-quality feedback while they still have the context necessary to fix problems immediately. Instead of waiting for external pipelines to run minutes later, agents can iteratively improve code within seconds, reducing wasted compute and improving the likelihood that pull requests pass downstream checks on the first attempt.
The launch reflects broader changes in software engineering workflows. CircleCI points to internal observations showing that feature branch activity has increased significantly as AI tools accelerate code generation, while deployments to production have not kept pace. The implication is that software delivery pipelines, testing infrastructure, and quality gates are increasingly becoming the limiting factor rather than code creation itself.
Chunk Sidecars are designed to alleviate this pressure by allowing agents to perform many CI-like validations locally in isolated environments before initiating full pipelines. CircleCI pairs the feature with Chunk Microbuilds, lightweight validation runs that execute subsets of pipeline logic, providing faster feedback at lower cost. Together, the technologies aim to improve software quality while reducing the amount of failed work entering central CI systems.
Chunk Sidecars are part of CircleCI's broader AI strategy centered on Chunk, the company's autonomous CI/CD agent. Earlier in 2026, CircleCI introduced capabilities that allow Chunk to analyze pipeline execution history, identify performance bottlenecks, optimize build configurations, and automatically propose fixes. With Sidecars, CircleCI is extending that intelligence into the development process itself, enabling agents not just to optimize pipelines, but to continuously validate their own output.
The company describes this as a shift from CI/CD pipelines acting as external checkpoints to becoming active collaborators in AI-assisted software development. Rather than treating validation as a separate stage after coding is complete, the goal is to integrate quality checks directly into the AI agent's workflow so that correctness evolves alongside code generation.
CircleCI is not alone in pursuing this vision. Companies across the software industry are building platforms that enable AI agents to operate within controlled, validated engineering environments. Dropbox recently introduced its Nova platform, where coding agents execute within isolated sessions tied to real build systems and validation workflows. GitHub continues expanding support for AI-assisted development through Copilot and MCP-based tooling, while Anthropic's Claude Code emphasizes tool usage and iterative validation during coding sessions.
What differentiates CircleCI's approach is its focus on leveraging existing CI/CD expertise and infrastructure to support agentic workflows. Instead of replacing pipelines, Chunk Sidecars extend them into the earliest phases of development, effectively creating miniature CI environments that accompany agents as they work. This strategy acknowledges a growing industry consensus: as AI accelerates code generation, validation, and trust increasingly become the primary engineering challenges rather than coding itself.
The release also hints at a broader transformation underway in software engineering. Increasingly, code may be written by one AI agent, validated by another, optimized by a third, and finally reviewed by humans only at critical checkpoints. In such a world, CI/CD platforms must evolve from passive automation systems into active participants in the software lifecycle.
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