InfoQ Homepage AI Development Content on InfoQ
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Embabel Agent Framework Reaches 1.0
Embabel has reached its 1.0 release, providing a framework for AI agents on Java It allows Java and Kotlin developers to define agents as typed domain objects. Built on Spring AI, Embabel supports multiple model providers and combines planning with predefined state machines, offering flexibility for agent workflows.
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Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation
Stripe introduces a benchmark suite to evaluate whether AI agents can build real-world Stripe integrations across backend, frontend, and browser-based checkout workflows. The study examines end-to-end software engineering capability, focusing on execution, testing, and validation gaps in agentic systems under production-like constraints.
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AI Is Moving up the Software Lifecycle: from Code Review to PRD Governance
Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline.
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AI-Assisted Migration Tool Helps Teams Move from ingress-nginx to Higress in Minutes
The Cloud Native Computing Foundation has highlighted a new AI-assisted migration approach that enabled engineers to migrate 60 ingress-nginx resources to Higress in roughly 30 minutes, demonstrating how artificial intelligence is increasingly being applied to modernize Kubernetes networking and gateway infrastructure.
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Pullfrog AI: Open-Source CodeRabbit Alternative Powered by GitHub Actions
Pullfrog is an open-source AI-powered GitHub bot by Colin McDonnell, designed for automation in GitHub Actions. It supports a model-agnostic approach, allowing integration with various LLM providers. Key features include orchestration for pull request reviews, issue triage, and CI remediation, all managed within GitHub's environment. The tool operates with a bring-your-own-key model for access.
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Inside Claude Code Auto Mode: Anthropic’s Autonomous Coding System with Human Approval Gates
Anthropic has introduced auto mode in Claude Code, enabling multi-step software development workflows with reduced manual intervention. The feature combines automated execution with layered safety mechanisms, including input filtering, action evaluation, and two-stage classification, while maintaining human approval checkpoints for sensitive operations.
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Uber Migrates 75,000+ Test Classes from Junit 4 to Junit 5 Using Automated Code Transformation
Uber engineers migrated over 75,000 test classes from JUnit 4 to JUnit 5 using automated code transformation with OpenRewrite and internal orchestration. By enabling the JUnit Platform for dual execution with Bazel and validating changes through CI, the team modernized testing infrastructure while maintaining correctness at monorepo scale.
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Anthropic Introduces Managed Agents to Simplify AI Agent Deployment
Anthropic introduces Managed Agents on Claude, a managed execution layer for agent-based workflows. It separates agent logic from runtime concerns like orchestration, sandboxing, state management, and credentials. The system supports long-running multi-step workflows with external tools, error recovery, and session continuity via a meta-harness architecture.
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Designing Memory for AI Agents: inside Linkedin’s Cognitive Memory Agent
LinkedIn introduces Cognitive Memory Agent (CMA), generative AI infrastructure layer enabling stateful, context-aware systems. It provides persistent memory across episodic, semantic, and procedural layers, supporting multi-agent coordination, retrieval, and lifecycle management. CMA addresses LLM statelessness and enables production-grade personalization and long-term context in AI applications.
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Cursor 3 Introduces Agent-First Interface, Moving beyond the IDE Model
Anysphere released Cursor 3, a redesigned interface built from scratch that shifts the primary model from file editing to managing parallel coding agents. The new workspace supports local-to-cloud agent handoff, multi-repo parallel execution, and a plugin marketplace. Community reaction has been divided, with developers questioning cost overhead and the move away from Cursor's IDE-first identity.
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GitHub Copilot CLI Reaches General Availability
GitHub has launched Copilot CLI into general availability, bringing generative AI directly to the terminal. Integrated with the GitHub CLI, it offers natural language command suggestions and code explanations. Recent updates introduce "agentic" workflows with Autopilot mode and GPT-5.4 support, alongside new enterprise telemetry for tracking usage across development teams.
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GitHub Integrates AI to Improve Accessibility Issue Management and Automate Feedback Triage
GitHub has launched a continuous AI-powered workflow to manage accessibility feedback at scale. Using GitHub Actions, Copilot, and Models APIs, the system centralizes reports, analyzes WCAG compliance, and automates triage while maintaining human validation. Teams now resolve feedback faster, improving inclusion and cross-functional collaboration.
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QCon London 2026: Tools That Enable the Next 1B Developers
At QCon London 2026, Ivan Zarea, director of platform engineering at Netlify, discussed the impact of AI on web development, noting a surge in non-traditional developers among the 11 million users on the platform. He presented three pillars for developer tools: developing expertise, honing taste, and practicing clairvoyance, emphasizing the need for thoughtful architecture in a evolving landscape.
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QCon London 2026: Running AI at the Edge - Running Real Workloads Directly in the Browser
At QCon London 2026, James Hall discussed running AI workloads directly in browsers, highlighting local processing benefits such as enhanced privacy, reduced latency and cost. He examined technologies like Transformers.js and WebGPU, illustrated practical applications, and provided guidelines for browser-based AI implementation, emphasizing appropriate use cases and evaluation principles.
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QCon London AI Coding State of the Game: More Capable, More Expensive, More Dangerous Coding Agents
In her QCon London keynote, Birgitta Böckeler, AI-Coding lead at Thoughtworks, reflected on the changes in the AI coding space over the past year. She emphasised a shift from vibe coding to using autonomous coding agents or swarms of agents. According to her, two major concerns in the field are the worsening security landscape and the rising costs of agent-based development.