Agentic Coding | Techlist.io

gitlabJul 16, 20263 min readCurated summary

Forrester Consulting: GitLab Duo Agent Platform delivers 400% ROI

GitLab-commissioned Forrester research found that organizations using GitLab Duo Agent Platform could achieve a 400% three-year ROI, $7.5 million in net present value, and payback in under six months. The study argues that agentic coding creates the greatest business value when integrated across the software lifecycle—not merely used to generate code. Benefits included faster onboarding, shorter migrations, quicker security remediation, and more developer time for feature work. ## Study Scope and Financial Model - Forrester interviewed four organizations across financial services, software, entertainment, and insurance. - Their experiences were modeled as a composite global company with: - $3 billion in annual revenue - 3,000 employees - GitLab Duo Agent Platform adoption growing from 150 to 250 users - Three-year risk-adjusted costs totaled approximately $1.9 million: - $1.3 million in consumption credits - $589,000 for implementation, training, support, and internal labor - Quantified benefits reached $9.4 million, producing: - 400% ROI - $7.5 million net present value - Payback in less than six months ## Problems Before Adoption - Manual processes and ad-hoc knowledge sharing slowed development. - New developers depended heavily on senior engineers for context and troubleshooting. - Security fixes waited in queues for specialists with the necessary expertise. - Code review was often a larger bottleneck than writing code. - Senior engineers were repeatedly interrupted to unblock other team members. ## Quantified Benefits - **80% faster developer onboarding** - Agentic chat in IDEs and repositories helped new hires understand unfamiliar codebases independently. - Estimated savings: $582,000. - **75% shorter migration timeline** - A migration from on-premises GitLab to GitLab SaaS finished in two months instead of eight. - Agents helped diagnose pipeline failures and resolve issues during the migration. - Estimated labor savings: $157,000. - **40% more time for security and QA engineers** - Contextual explanations and suggested fixes reduced remediation effort and reliance on senior staff. - Estimated three-year savings: $1.3 million. - **20% more developer capacity for feature work** - Agents supported code review, testing, and troubleshooting. - Estimated combined benefit: $7.4 million. - Additional unquantified benefits included reduced need for overlapping AI tools, improved developer satisfaction, and stronger knowledge sharing. ## Broader Impact on Software Delivery The study found that organizations shipped features in days rather than weeks, resolved vulnerabilities more quickly, onboarded staff faster, and compressed major infrastructure work. Its central conclusion is that AI productivity gains compound when agentic coding is connected to infrastructure supporting the entire software delivery lifecycle. The findings are based on interviewed organizations and a composite financial model, so actual results will vary. Companies evaluating agentic development should use the study as a framework for estimating benefits across productivity, security, onboarding, and operational efficiency—not as a guaranteed ROI.

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Agentic Coding AI Agents CI/CD DevSecOps+3

googleMay 28, 20263 min readCurated summary

A New Era of Innovation: Google Research at I/O 2026

Google’s I/O 2026 research announcements present AI as an “agentic” amplifier of human ingenuity, particularly in science and healthcare. New systems such as Gemini for Science, ERA, Co-Scientist, and Gemini Deep Think are designed to generate hypotheses, write and optimize code, evaluate evidence, and solve difficult research problems. Google also highlighted health-focused AI that supports users before, during, and after medical visits, while emphasizing collaboration, validation, and responsible deployment. ## AI-Driven Scientific Discovery - **Gemini for Science** is a suite of experimental tools built from Google Research and developed with Google Cloud, Google DeepMind, and Google Labs. - **Empirical Research Assistance (ERA)** acts as a code-optimizing research engine: - Proposes concepts and writes software. - Evaluates results against a defined scoring system. - Uses tree search to test thousands of code variants. - Has supported work in neuroscience, cosmology, respiratory-illness forecasting, and California runoff prediction. - **Co-Scientist** is a Gemini-based multi-agent collaborator that generates, evaluates, and refines hypotheses. - Researchers have applied it to antimicrobial resistance, plant immunity, and liver fibrosis. - **Computational Discovery**, combining ERA and AlphaEvolve, runs thousands of code variations in parallel to test scientific models and hypotheses more quickly. - **Hypothesis Generation** uses a multi-agent “idea tournament” to debate and rank research ideas, with clickable citations supporting claims. - **Literature Insights**, powered by NotebookLM, helps researchers synthesize large bodies of scientific literature. - **Science Skills** can automate specialist workflows such as structural bioinformatics and genomic analysis on agentic coding platforms. ## AI for Peer Review and Advanced Reasoning - Google is piloting the **Paper Assistant Tool (PAT)** for scientific peer review. - PAT has experimentally reviewed more than 10,000 papers for conferences including ICML, STOC, and NeurIPS. - Its feedback has helped authors identify theoretical gaps and design additional experiments. - **Gemini Deep Think** has been used with mathematicians, physicists, and computer scientists to address open problems involving network deadlocks, optimization, machine-learning behavior, auction theory, and cosmic-string singularities. ## Advancing Health with AI - Google’s health research focuses on supporting people throughout the full healthcare journey, from understanding symptoms and preparing for appointments to interpreting medical records. - Research contributions underpin the **Google Health app** and **Google Health Coach**, with the app beginning rollout to existing Fitbit users. - **Symptom AI** investigates how conversational AI can reason about information relevant to a person’s symptoms. - A Fitbit-based study included 13,917 participants. - In blind comparisons, clinicians preferred Symptom AI’s differential diagnoses roughly twice as often as those produced by other clinicians. - The **Plan for Care** pilot involved 1,779 participants preparing for doctor visits. - Compared with baseline systems, 15% more users felt prepared. - 13% more users felt confident they could make effective use of their appointment. - Google is also studying personal health large language models and the use of personal health record data to improve health guidance. Google’s announcements point toward research systems that actively experiment, collaborate, and reason rather than merely retrieve information. Their practical value will depend on continued scientific validation, clinician involvement, privacy protections, and careful expansion from experimental tools into real-world use.

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Agentic Coding Machine Learning Generative AI Gemini+2

gitlabMay 28, 20263 min readCurated summary

Agentic coding is only as good as its context

Coding agents are only as effective as the context they can access. Repository-only agents may produce compiling fixes, but they often miss issue requirements, CI rules, security policies, and review standards. Integrating agents with GitLab’s issues, merge requests, pipelines, and security controls produces better-aligned code, fewer review cycles, and safer releases while preserving human approval. ## From Repository Context to Full Lifecycle Context - GitLab tutorials demonstrate progressively richer workflows using Claude Code and Codex. - **Repository only:** The agent reads local files, interprets the prompt, and runs the build, but lacks acceptance criteria, organizational requirements, and review expectations. - **Repository plus issue:** Through GitLab’s MCP server, the agent can read issue descriptions, labels, milestones, and implementation notes. - It can correctly link changes to issues, such as adding `Closes #32`. - Tools such as `get_issue` and `create_merge_request` connect implementation work to planned work. - **Working inside the merge request:** GitLab’s Code Review Flow provides feedback, which the agent can address by adding tests, improving documentation, and fixing validation gaps. - New commits automatically trigger CI/CD. - Human reviewers can inspect the result in the same merge request. - The tutorials report fewer review rounds and shorter merge times. ## Why Platform Visibility Matters - Platform teams determine: - Which agents and tools are permitted - How agent output is verified - Where human approval is required - Important context lives in the DevSecOps platform: - Issues define requirements. - CI/CD configuration defines quality standards. - Review instructions establish coding conventions. - Security scanners enforce vulnerability policies. - Merge requests bring automation and human review together. - IDE- or terminal-based agents generally see only the files provided to them, while the platform sees the full lifecycle, including deployment targets and approval rules. - Consequently, the platform—not the agent alone—largely determines what can ship safely. ## Security Implications of Faster Code Generation - Agents produce code and remediation patches faster, increasing both the number of vulnerabilities and the number of security fix merge requests. - The bottleneck shifts from finding vulnerabilities to deciding which AI-generated fixes should be reviewed and approved first. - Effective prioritization requires broader context, including: - Application data flows - Deployment environments - Project-wide code - Organization-wide security policies - GitLab’s security layer can filter false positives, confirm vulnerabilities, and use surrounding repository context to propose fixes. - Agentic SAST vulnerability resolution creates a merge request with the proposed remediation. - CI/CD validates the change, while a human reviewer retains final approval. - Strong quality gates and security controls applied within merge requests reduce the chance that vulnerabilities reach production. ## Repository Instructions with `AGENTS.md` - The tutorials use `AGENTS.md` to provide project-specific instructions. - These files can describe: - Repository structure - Commands to run - Code-quality expectations - Files or areas that must not be changed - Language and framework conventions - CI image-pinning requirements - Concurrency patterns - Custom instructions help agents operate consistently with project standards, even before they receive broader platform context. Organizations should connect coding agents to the systems that define requirements, quality, security, and approval. Repository access may enable a working patch, but lifecycle context is what makes that patch trustworthy and ready to merge.

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Agentic Coding CI/CD Gitlab Model Context Protocol+3

lineApr 24, 20264 min readCurated summary

ODW #4: From Copilot to Pilot, Automating from Implementation to PR with Agentic Coding

LY Corporation’s Orchestration Development Workshop promotes a shift from using AI as a code suggestion tool to using it as an autonomous development “pilot.” The team combined specification-driven development with Jira/Confluence access through MCP so AI agents could investigate requirements, plan implementations, write code, run checks, create pull requests, and respond to reviews. The approach improved development speed and planning, but still requires humans to review AI-generated code and retain responsibility for product quality. ## From Copilot to Agentic Coding - The team initially used GitHub Copilot for small code-generation tasks but saw limited productivity gains. - Two developments enabled a broader shift: - Existing requirements and design documents supported specification-driven development. - Jira and Confluence became accessible to AI coding tools through the Model Context Protocol (MCP). - Agentic coding gives an AI agent a high-level goal, which it decomposes into tasks and executes iteratively. - Unlike autocomplete tools, an agent can analyze the broader codebase, run commands and tests, fix lint errors, and continue until the requested feature is complete. ## Workshop Goals and Human–AI Responsibilities - The workshop focused on automating the process from implementation through pull-request creation. - Humans remained responsible for: - Defining requirements and writing specifications - Final testing, review, and release decisions - AI was assigned: - Implementation planning - Code implementation - Pull-request creation - Initial review and review-response work - This division preserved the existing development process while giving participants practical experience with agentic workflows. ## Stage 1: Research and Implementation Planning - Participants supplied a Jira ticket URL to a custom slash command. - The AI agent: - Retrieved Jira data through the Jira MCP tool - Followed Epic links and collected related tickets - Retrieved Confluence documentation through the Confluence MCP tool - Explored the codebase, using an Explore Agent where available - Wrote a detailed implementation plan to `specs/{ticket-or-topic}/plan.md` - The plan included requirements, affected components, technical analysis, implementation tasks, risks, testing considerations, and a checklist. - Saving the plan to a file made it available for human review, future sessions, and later PR generation. - The team emphasized planning early because vague instructions can lead to incorrect implementations and costly rework. ## Stage 2: Implementation and Pull-Request Creation - The reviewed plan was passed to an implementation command. - The AI was instructed to: - Understand the plan and implementation scope - Modify the code - Add or update tests - Run the test suite - Run linting and build commands - Fix any resulting problems - Explicitly listing these steps encouraged the coding agent to maintain a task checklist and complete the full development cycle. - A separate PR command generated the pull request using the team’s template. - Information gathered during the planning stage could be reused in the PR description, reducing administrative work. ## Stage 3: AI Review and Issue Resolution - An AI screening-review command analyzed the generated PR. - It also read existing comments, including: - The AI’s own prior review comments - Comments from other team members - The agent identified issues requiring changes and explained its assessment of existing comments. - After a human reviewed those conclusions, the AI could implement the necessary fixes, reducing the cost of responding to review feedback. ## Benefits and Risks - **Higher code-generation speed** - Agents can work with less frequent human intervention. - Developers can perform other tasks while agents work. - Multiple agents can potentially run in parallel. - **Earlier risk discovery** - Detailed implementation plans clarify the work before coding begins. - Planning can reveal overlooked tasks, dependencies, and risks. - **Greater review burden** - AI can generate large volumes of code that humans must still inspect. - The unfamiliar workflow may create stress for developers. - **Human accountability remains essential** - Developers are responsible for the quality of AI-generated code. - Poor-quality output increases reviewer workload and can add technical debt. ## Workshop Results - The workshop was delivered twice: - A hands-on practical session requiring prior preparation - An introductory session with more detailed support - Approximately 2,500 people participated. - More than 40% of respondents had already applied, or intended to apply, some aspect of the workshop. - The sessions provided concrete guidance on MCP server usage and effective ways to delegate coding tasks to AI agents. The recommended approach is to introduce agentic coding incrementally: keep human ownership of requirements and final quality decisions, while allowing AI to handle structured planning, implementation, testing, PR creation, and initial review.

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Agentic Coding AI Model Context Protocol Github Copilot+4

awsApr 16, 20263 min readCurated summary

Introducing Anthropic’s Claude Opus 4.7 model in Amazon Bedrock | Amazon Web Services

Claude Opus 4.7 is now available in Amazon Bedrock as Anthropic’s most capable Opus model for coding, professional knowledge work, long-running agents, and visual tasks. It runs on Bedrock’s new inference engine, which dynamically schedules capacity, improves availability, queues requests during demand spikes, and provides zero operator access to prompts and responses. AWS positions the model as a production-ready upgrade to Opus 4.6, though teams may need to adjust prompts and agent harnesses. ## Model Improvements - **Agentic coding:** Stronger long-horizon autonomy, systems engineering, and complex code reasoning. - 64.3% on SWE-bench Pro - 87.6% on SWE-bench Verified - 69.4% on Terminal-Bench 2.0 - **Knowledge work:** Better document creation, financial analysis, and multi-step research. - Handles underspecified requests by making and explaining reasonable assumptions. - Self-verifies responses to improve first-pass quality. - 64.4% on Finance Agent v1.1. - **Long-running tasks:** Maintains coherence across its full 1-million-token context window while handling ambiguity and verifying results. - **Vision:** High-resolution image support improves analysis of charts, dense documents, and detailed screen interfaces. - **Adaptive thinking:** Dynamically allocates reasoning-token budgets based on request complexity. ## Bedrock Infrastructure and Scaling - The new inference engine uses updated scheduling and scaling logic to allocate capacity dynamically. - It prioritizes steady-state workloads while rapidly scaling capacity for changing demand. - During high demand, requests are queued instead of rejected. - Up to 10,000 requests per minute per account and Region are available immediately, with additional capacity available by request. - Zero operator access prevents AWS and Anthropic operators from viewing customer prompts and responses. ## Ways to Invoke the Model - Test Claude Opus 4.7 in the Amazon Bedrock console through **Playground**. - Use the Anthropic Messages API through: - Anthropic SDK integrations with Bedrock - `bedrock-mantle` endpoints - Use Bedrock-native APIs: - **Converse API** for multi-turn conversations and Guardrails integration - **Invoke API** for direct invocation and lower-level control - Invoke it with the AWS CLI using model ID `anthropic.claude-opus-4-7`. - The Anthropic Bedrock SDK can authenticate with AWS Signature Version 4 and submit Messages API requests programmatically. ## Availability - Initially available in: - US East (N. Virginia) - Asia Pacific (Tokyo) - Europe (Ireland) - Europe (Stockholm) - AWS directs users to the Bedrock model and pricing pages for regional availability updates. Teams seeking stronger coding agents, complex research workflows, or high-volume production inference can try Claude Opus 4.7 in Bedrock, while validating prompt and harness changes before migrating existing Opus 4.6 workloads.

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Agentic Coding Python Amazon Bedrock API+4

googleNov 17, 2025Original article

Generative UI: A rich, custom, visual interactive user experience for any prompt (opens in new tab)

Google Research has introduced a novel Generative UI framework that enables AI models to dynamically construct bespoke, interactive user experiences—including web pages, games, and functional tools—in response to any natural language prompt. This shift from static, predefined interfaces to AI-generated environments allows for highly customized digital spaces that adapt to a user's specific intent and context. Evaluated through human testing, these custom-generated interfaces are strongly preferred over traditional, text-heavy LLM outputs, signaling a fundamental evolution in human-computer interaction. ### Product Integration in Gemini and Google Search The technology is currently being deployed as an experimental feature across Google’s main AI consumer platforms to enhance how users visualize and interact with data. * **Dynamic View and Visual Layout:** These experiments in the Gemini app use agentic coding capabilities to design and code a complete interactive response for every prompt. * **AI Mode in Google Search:** Available for Google AI Pro and Ultra subscribers, this feature uses Gemini 3’s multimodal understanding to build instant, bespoke interfaces for complex queries. * **Contextual Customization:** The system differentiates between user needs, such as providing a simplified interface for a child learning about the microbiome versus a data-rich layout for an adult. * **Task-Specific Tools:** Beyond text, the system generates functional applications like fashion advisors, event planners, and science simulations for topics like RNA transcription. ### Technical Architecture and Implementation The Generative UI implementation relies on a multi-layered approach centered around the Gemini 3 Pro model to ensure the generated code is both functional and accurate. * **Tool Access:** The model is connected to server-side tools, including image generation and real-time web search, to enrich the UI with external data. * **System Instructions:** Detailed guidance provides the model with specific goals, formatting requirements, and technical specifications to avoid common coding errors. * **Agentic Coding:** The model acts as both a designer and a developer, writing the necessary code to render the UI on the fly based on its interpretation of the user’s prompt. * **Post-Processing:** Outputs undergo a series of automated checks to address common issues and refine the final visual experience before it reaches the browser. ### The Shift from Static to Generative Interfaces This research represents a move away from the traditional software paradigm where users must navigate a fixed catalog of applications to find the tool they need. * **Prompt-Driven UX:** Interfaces are generated from prompts as simple as a single word or as complex as multi-paragraph instructions. * **Interactive Comprehension:** By building simulations on the fly, the system creates a dynamic environment optimized for deep learning and task completion. * **Preference Benchmarking:** Research indicates that when generation speed is excluded as a factor, users significantly prefer these custom-built visual tools over standard, static AI responses. To experience this new paradigm, users can select the "Thinking" option from the model menu in Google Search’s AI Mode or engage with the Dynamic View experiment in the Gemini app to generate tailored tools for specific learning or productivity tasks.

Agentic Coding AI Large Language Models Gemini+4

figmaNov 5, 20252 min readCurated summary

Issue No.13: All Systems Go | Figma Blog

Design systems are becoming essential infrastructure as teams move faster and adopt AI-assisted design and coding tools. Figma argues that well-structured systems extend quality and design intent across human and machine-generated work. The issue highlights how Figma’s products and customer practices are helping teams design, build, and collaborate more reliably in this new environment. ## Unlocking Design Systems - Schema by Figma emphasized that design systems now serve AI as well as human designers. - Figma is adding design-system context to Figma Make to improve generated results. - Its MCP server supports more effective agentic coding workflows. - The broader goal is to produce more consistent, polished, and reliable outputs as teams scale. ## Forging New Paths with Figma Make - Figma Make enables teams to move beyond traditional design workflows by working more directly in code. - Companies including Maven Clinic, Pendo, ServiceNow, and LinkedIn are using it to rethink: - Product roadmaps - Product requirements documents - Starter templates - Design-system deployment - Designers can use prompts to explore ideas, prototype faster, and create new paths for collaboration. ## Clarifying Design Context - As developers increasingly rely on AI coding agents, design files need to communicate more than visual appearance. - Clear file organization, meaningful structure, and annotations help developers—and AI tools—understand design intent. - Providing strong context allows coding tools to implement designs more accurately and efficiently. - Designers should treat their files and systems as shared sources of information, not merely presentation artifacts. ## Further Reading - Figma points readers to foundational design-system material covering basic principles through AI-driven automation. - It also recommends guidance for design leaders building resilient teams and helping designers adapt to rapid change. Strong design systems, combined with clear context and modern tools like Figma Make and MCP, can help teams preserve craft while increasing speed. The practical recommendation is to build systems that are understandable, reusable, and structured for both people and AI.

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Agentic Coding AI Design Systems Model Context Protocol+1

figmaJun 10, 20252 min readCurated summary

The Long and Short of It: Issue no.11 | Figma Blog

AI is changing how people build, but Figma argues that it does not replace craft, quality, or emotional connection. The issue highlights tools and practices that combine automation with design intent, context, safety, and care. Its central conclusion is that meaningful work still requires human judgment and deliberate practice. ## Building with Figma Make - Figma Make is a prompt-to-code tool that turns natural-language prompts or static designs into interactive prototypes. - It can be used at different stages of the design and development process. - Figma recommends experimenting with prompts and applying practical techniques to get better results. ## Bringing Design Context into Coding - Agentic coding tools are limited when they lack context about the intended design. - Figma’s MCP server connects Figma files to AI coding tools. - It gives language models access to variables, components, and styles, helping them generate code that better reflects design intent. - This positions AI as part of the developer workflow rather than an isolated automation tool. ## When Efficiency Undermines Care - The issue examines the idea that excessive focus on efficiency can weaken connection and emotional investment in creative work. - Themes from Config 2025 include AI evolving from a tool into a teammate and the importance of reaching “minimum viable play.” - The broader message is that faster production should not come at the expense of thoughtful, resonant design. ## Designing AI with Trust and Transparency - Headspace’s Ebb AI mental-health companion was designed with trust and safety as priorities. - Product and brand teams considered details such as the character’s name, visual identity, and conversational guidelines. - The goal was to keep Ebb’s AI nature visible while ensuring users felt supported. - The example illustrates the additional responsibility involved in creating AI products for sensitive contexts. ## Craft Requires Practice - Figma’s third annual Config publication, *Practice*, explores how designers develop mastery. - It emphasizes patience, precision, experimentation, and a willingness to push boundaries. - The accompanying microsite, created with Other Means, includes a custom font by Kia Tasbihgou. ## Rabbit Hole - The issue closes with a visual collection of colorful abstract forms, collages, and photography, extending its focus on experimentation and creative inspiration. AI can accelerate making, but strong results still depend on context, care, taste, and practice. Figma’s recommendation is to use AI as a creative partner while preserving the human attention that gives work its meaning.

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Agentic Coding Figma AI Large Language Models+3