Anthropic Shifts Focus to Agentic AI

Anthropic's "Code with Claude 2026" event shifted focus from raw model power to agentic capabilities, introducing "Dreaming" and multi-agent orchestration. Google launched Gemini Spark, a proactive personal AI agent, alongside a new "Neural Expressive" design language. GitHub Copilot expanded its SDK, CLI, and agentic features, including a new usage-based billing model. The industry is clearly moving towards highly integrated, autonomous agents that manage complex workflows across platforms.

Top of the Week

Anthropic’s "Code with Claude 2026" event, held across San Francisco, London, and Tokyo, signaled a deliberate pivot: no new frontier models were announced. Instead, the focus was entirely on agentic features, with five major releases: Dreaming, Outcomes, multi-agent orchestration, Claude Finance with 10 pre-built agents, and Add-ins. This move emphasizes the "harness race" — the development of scaffolding and tooling around existing capable models — over the "frontier model race." Anthropic argues that the real competition now lies in how effectively AI can perform complex, long-running tasks, particularly in software engineering and finance.

The company also released Claude Opus 4.7, which is now generally available. Opus 4.7 shows marked improvements in advanced software engineering and complex coding tasks, along with better vision capabilities. This model, notably, includes safeguards designed to detect and block requests indicating prohibited or high-risk cybersecurity uses, a direct result of learnings from their Project Glasswing and the limited release of Claude Mythos Preview. Meanwhile, Google launched Gemini Spark, a 24/7 personal AI agent designed to proactively manage digital tasks, rolling out to trusted testers and U.S. Google AI Ultra subscribers. Spark aims to transform Gemini from a reactive assistant into an active partner that performs work on your behalf, operating in the background even when the phone is locked.

This shift across both Anthropic and Google highlights a critical maturation in the AI landscape: the industry is moving beyond raw chatbot capabilities to focus on autonomous, multi-step agents that can integrate deeply into user workflows and enterprise systems. For builders, this means the value now lies less in incrementally better base models and more in intelligent orchestration, robust tooling, and domain-specific applications that can execute complex tasks with minimal human intervention.

Claude

• Agentic Dreaming — Anthropic introduced "Dreaming," an agentic feature designed to help Claude plan and anticipate future steps in complex tasks. This moves Claude beyond reactive responses, allowing it to strategize and verify its own outputs before reporting back. It matters because it enables more reliable, long-running agentic workflows, reducing the need for constant human oversight in multi-step processes.
• Outcomes-Driven Execution — The new "Outcomes" feature allows users to define specific goals, and Claude will work towards achieving them, dynamically adjusting its approach. This shifts interaction from conversational prompts to goal-oriented directives, making Claude a more effective tool for project completion. This matters for developers building applications that require AI to manage and adapt to evolving task requirements.
• Multi-Agent Orchestration — Anthropic unveiled capabilities for Claude to orchestrate multiple sub-agents within a single session, enabling parallel execution of hundreds of tasks. This feature, particularly with Dynamic Workflows for Enterprise, Team, and Max plans, allows for codebase-scale migrations and other large-scale operations. This matters for organizations tackling complex, distributed problems that benefit from parallel AI processing.
• Claude Finance with Pre-Built Agents — A new vertical offering, Claude Finance, launched with 10 pre-built agents and connectors to industry-specific data sources like Dun & Bradstreet, Fiscal AI, and Verisk. These agents are designed for production financial workflows, such as pitch building and market analysis. This matters as it targets specific, high-value enterprise use cases, demonstrating how AI can automate low-skill, repetitive knowledge work in specialized domains.
• Add-ins for Extended Functionality — Anthropic introduced "Add-ins," allowing Claude to integrate with external tools and services, expanding its capabilities beyond its core model. This modular approach enables developers to extend Claude's reach into existing software ecosystems. This matters for creating highly customized AI solutions that interact seamlessly with a user's existing tech stack.
• Opus 4.7 General Availability — Claude Opus 4.7 is now generally available, bringing significant improvements in advanced software engineering and complex, long-running coding tasks. It also features enhanced vision capabilities for higher-resolution image understanding. This matters for developers and engineers who rely on AI for demanding coding challenges and visual analysis.
• Cybersecurity Safeguards in Opus 4.7 — Opus 4.7 includes new safeguards that automatically detect and block requests indicating prohibited or high-risk cybersecurity uses. These measures stem from Project Glasswing and aim to responsibly deploy advanced AI while mitigating potential misuse. This matters for ensuring the ethical and secure deployment of powerful AI models, especially as capabilities grow.
• Claude Design Launch — Anthropic Labs launched Claude Design alongside Opus 4.7, enabling collaborative visual output creation like designs, prototypes, slides, and one-pagers. This product aims to make Claude a partner in visual content generation. This matters for creative professionals and marketers looking to integrate AI into their design workflows.
• Memory for All Tiers — Claude's memory feature, which allows context-aware conversations across sessions, is now available for all users, including the free tier. A memory import tool also lets users transfer chat history and preferences from other AI platforms. This matters for a more consistent and personalized user experience, making Claude feel like an ongoing working relationship rather than a stateless tool.
• Mid-Conversation System Messages — The Messages API now accepts role: "system" messages mid-conversation, enabling instruction updates without restating the full prompt. This preserves prompt cache hits and reduces cost in agentic loops. This matters for developers building complex, dynamic agents that need to adapt instructions on the fly while optimizing token usage.

Gemini

• Gemini Spark Personal AI Agent — Gemini Spark, a 24/7 personal AI agent, is now rolling out to trusted testers and in Beta to Google AI Ultra subscribers aged 18 and over in the United States. Spark transforms Gemini from an assistant into a proactive partner that works on tasks on your behalf, operating in the background even when your phone is locked. This matters for users seeking autonomous task management and a more agentic AI experience.
• Neural Expressive Design Language — Google has redesigned the entire Gemini experience with a new "Neural Expressive" design language, featuring fluid animations, vibrant colors, new typography, and haptic feedback. This visual overhaul aims for a more intuitive and engaging user interface. This matters for user adoption and making complex AI interactions feel more natural and accessible.
• Daily Brief Feature — A new "Daily Brief" feature is rolling out to Google AI subscribers in the United States, providing a personalized digest of information from a user’s inbox, calendar, and most important tasks. It prioritizes tasks and suggests next steps. This matters for productivity, offering a curated, proactive start to the day by synthesizing critical information.
• Gemini Omni for Video Creation — Gemini Omni was introduced as a new creative partner for video creation. While details are sparse, this signals Google's expansion into multimodal AI beyond text and images, specifically targeting video generation. This matters for creators and businesses looking to automate or enhance video production workflows with AI.
• Gemini 3.5 Flash in Search — Google Search is now upgraded with Gemini 3.5 Flash as the new default model in AI Mode globally. This model, known for sustained frontier performance in agents and coding, powers an intelligent Search box redesigned to put powerful AI tools at users' fingertips. This matters for delivering faster, more accurate search results and enabling complex queries directly within the search interface.
• Agentic Coding in Search — The power of Google Antigravity and the agentic coding capabilities of Gemini 3.5 Flash are integrated into Search, allowing it to build ideal responses in the right format, including custom generative UI, visual tools, and simulations. These generative UI capabilities will be available free this summer. This matters for developers and researchers who need dynamic, tailored visual and interactive outputs directly from search queries.
• Expanded MCP Connections — Gemini Spark is expanding its list of connected apps with new MCP (Model Context Protocol) integrations to Canva, OpenTable, and Instacart, with more partners integrating soon. These connections allow Spark to perform actions across various services. This matters for increasing the utility and reach of Gemini as a personal agent, enabling it to interact with a broader digital ecosystem.
• Gemini Code Assist Upgrade — Gemini Code Assist now uses the Gemini 1.5 Flash model (with a 32k token window), improving support for code explanation, unit test generation, and code transformations. Automatically triggered code completions use an 8k token window. This matters for developers seeking more intelligent and context-aware coding assistance within their IDEs.
• Gemini in Looker and BigQuery GA — Gemini in BigQuery features are now generally available, and Gemini in Looker is available in Public Preview for conversation analytics. These integrations bring AI-powered insights and translation capabilities directly into Google Cloud's data platforms. This matters for data professionals looking to leverage AI for enhanced data analysis, query translation, and business intelligence.
• Gemini for macOS Desktop App — Google is developing a Gemini app for macOS, which will bring Gemini Spark to the desktop this summer. This will enable Spark to assist with tasks involving local files and automate workflows across the desktop. This matters for integrating Gemini's agentic capabilities directly into the desktop environment, offering a more holistic AI assistant experience.

Copilot

• Copilot SDK in Technical Preview — GitHub Copilot SDK is now in technical preview, offering language-specific SDKs (Node.js/TypeScript, Python, Go,.NET) for programmatic access to Copilot CLI. This opens the door for developers to build on top of Copilot at scale. This matters for extending Copilot's capabilities and integrating AI assistance into custom development tools and workflows.
• New Usage-Based Billing Model — Starting June 1, 2026, GitHub Copilot is transitioning from Premium Request Units (PRUs) to a new usage-based billing model powered by GitHub AI Credits. This change requires organizations to re-evaluate how they manage and pay for AI-powered development. This matters for IT decision-makers and administrators who need to understand and prepare for the financial implications and management of Copilot adoption.
• Agents Window in VS Code Stable Preview — The Agents window is now available in VS Code Stable as a preview, providing an agent-first experience focused on completing tasks rather than just editing code. This includes improved support for remotely controlling longer-running, complex agent sessions. This matters for developers who want to delegate multi-step coding tasks to AI agents directly within their IDE.
• Copilot Memory for Agents — Copilot memory has arrived, allowing agents to learn about your code over time and retain context across sessions. This feature supports user preferences for Business and Enterprise plans. This matters for enhancing the intelligence and continuity of AI agents, making them more effective and personalized coding partners.
• GPT-5.2-Codex General Availability — The GPT-5.2-Codex model has graduated to General Availability, with wider availability across Visual Studio, JetBrains IDEs, Xcode, Eclipse, and GitHub.com. This provides developers with access to a more capable and widely supported code generation model. This matters for ensuring consistent, high-quality AI assistance across various development environments.
• Smarter Code Reviews with Linter Integration — Copilot's code review capabilities are enhanced to automatically check CodeQL and ESLint integrations. There are also rumors of more linters on the way. This improves the quality and consistency of code by integrating AI reviews with existing static analysis tools. This matters for maintaining code standards and catching issues earlier in the development cycle.
• Copilot CLI Enhancements — The Copilot CLI received UI improvements, "rubber duck" debugging, prompt scheduling, and voice input. It also gained ACP (Anthropic Compute Platform) support in preview and native installation via GitHub CLI. These enhancements make the command-line interface more powerful and user-friendly. This matters for developers who prefer command-line workflows and need advanced AI assistance outside of a full IDE.
• Chronicle for Session Insights — New /chronicle commands allow users to query past sessions, generate standup reports, and receive personalized productivity tips. This feature provides insights into agent activity and helps track progress. This matters for improving developer productivity and understanding how AI agents contribute to project goals.
• Cloud and Local Sandboxes (Public Preview) — GitHub Copilot now offers cloud and local sandboxes in public preview, enhancing security and governance for AI-assisted development. This allows for safer execution of AI-generated code and agent actions. This matters for enterprise security teams and developers working with sensitive codebases.
• Gemini 3 Flash Model Availability — Google's latest Gemini 3 Flash model is now selectable across Visual Studio, JetBrains IDEs, Xcode, and Eclipse within Copilot. This expands the choice of underlying models for developers, potentially offering different performance characteristics. This matters for developers who want flexibility in choosing the best-performing AI model for their specific coding tasks.

Tools Worth Trying

1. Midjourney V8.1 — This stability-focused update to the image generation tool makes HD mode 3x faster and cheaper, with standard resolution also seeing speed and cost improvements. New features like Prompt Shortener and an updated Describe make iteration quicker. An editing model is expected soon. Best for creative artwork and concept designs, aiming for high-quality visuals.
2. Perplexity Comet — Originally a premium product, Comet is now free on iOS, Android, Windows, and Mac. This browser integrates Perplexity’s answer engine directly with web content, offering context-aware tab assistance, voice mode, and multi-step agentic task automation. Its Deep Research feature now generates deliverables like presentations and spreadsheets. Ideal for real-time research with cited answers and agentic browsing.
3. NotebookLM — Google’s powerful free tool for research allows users to upload sources (PDFs, audio files, websites) to create a grounded AI expert on only that data. The free tier supports up to 100 notebooks, 50 sources per notebook, and 500,000 words total. Excellent for data practitioners who need to synthesize information from specific documents without hallucination.
4. Grok 4.1 Free Tier — xAI's free plan for Grok 4.1 chat models offers limited access, including real-time access to X (formerly Twitter) data, the Aurora image model, and voice access. While prompt limits apply (e.g., 10 text prompts every 2 hours), it's a strong alternative for up-to-the-minute news aggregation and direct answers. Best for users needing current information and unconstrained responses.
5. AutoGen — A multi-agent collaboration framework designed for research or complex tasks. It allows multiple AI agents to work together to solve problems, simulating human team dynamics. This is for developers and researchers building sophisticated AI systems that require collaborative problem-solving.
6. V0 — An intuitive and powerful tool for building web applications. It focuses on visual, no-code development, making it accessible for users without deep coding expertise. Ideal for rapid prototyping and deployment of web apps.

GitHub Copilot is adding a new user every second, contributing to a 20% jump in GitHub's developer base, reaching 36 million new developers this year alone.

The 5-Minute Action Plan

1. Explore Claude's New Agentic Features: Head to claude.ai and experiment with "Dreaming" and "Outcomes" to see how Claude can plan and execute multi-step tasks. Define a complex goal and observe its reasoning process. This is crucial for understanding the new paradigm of goal-oriented AI.
2. Test Gemini Spark's Proactive Capabilities: If you're a Google AI Ultra subscriber in the US, access the Spark tab in your Gemini app menu. Set up a custom workflow or task to see how it operates proactively in the background. This will give you a first-hand look at the "agentic Gemini era."
3. Review Copilot's New Billing Model: For organizations using GitHub Copilot, familiarize yourself with the transition from Premium Request Units (PRUs) to GitHub AI Credits, effective June 1, 2026. Consult the GitHub documentation to understand the new usage-based billing and prepare your budget.
4. Experiment with Copilot's Agents Window in VS Code: If you use VS Code, enable the Agents window (currently in preview) to experience the agent-first workflow. Try delegating a complex coding task to an agent and observe its execution and session management. This is key to leveraging Copilot's evolving agentic capabilities.
5. Utilize NotebookLM for Focused Research: Upload a few PDFs or web articles on a specific topic to NotebookLM. Ask it to summarize, extract key points, or generate questions based only on your provided sources. This helps ground AI responses and avoids hallucinations for critical information retrieval.
6. Try Gemini 3.5 Flash in Google Search AI Mode: Access Google Search and use AI Mode with complex, multi-part questions, especially those requiring visual or interactive outputs. Observe how Gemini 3.5 Flash generates custom UI or simulations. This showcases the enhanced intelligence and generative capabilities directly within search.

To significantly improve reasoning quality for complex problems, always add "Think step by step before responding" to your prompts. This forces the AI to break down the problem and process it logically. `` Explain [complex concept] as if I were 12 years old. Use analogies with everyday situations. Avoid technical terms, or if you use them, explain them immediately. Think step by step before responding. ``

The Prompt Library

Advanced Code Review with Linter Focus

Use this prompt to get a detailed code review that specifically integrates feedback from CodeQL and ESLint, ensuring adherence to best practices and catching common issues.

You are an expert Senior Software Engineer and Code Reviewer. I need you to perform a comprehensive code review of the following [language] code snippet.
Focus specifically on identifying potential issues related to CodeQL and ESLint rules, even if they are not explicitly flagged by a linter.
Provide actionable suggestions for improvement, including security vulnerabilities, performance bottlenecks, maintainability issues, and adherence to modern [language] best practices.
For each suggestion, explain the reasoning and provide a corrected code example or a clear description of how to fix it.
Also, identify any areas where the code could be made more readable or efficient.

Code:

### 2. Multi-Agent Task Orchestration Plan
*Use this prompt to plan a complex project by breaking it down into sub-tasks and assigning them to hypothetical specialized AI agents, outlining their roles and dependencies.*

You are a project manager specializing in AI-driven workflows. I need to complete the following complex project: "[PROJECT_GOAL]".

Break this project down into a series of distinct, sequential, or parallel sub-tasks.

For each sub-task, identify a hypothetical specialized AI agent (e.g., "Research Agent," "Coding Agent," "Design Agent," "Data Analysis Agent") that would be best suited to handle it.

Describe the specific input each agent would receive, the output it should produce, and any dependencies on other agents' outputs.

Finally, outline the overall orchestration flow, including how the outputs from different agents combine to achieve the main project goal.

### 3. Proactive Daily Brief Generation
*Use this prompt to generate a personalized daily brief that synthesizes information from various sources (e.g., calendar, inbox, news) and suggests prioritized next steps.*

You are a personal AI assistant. Generate a "Daily Brief" for me, focusing on the most important tasks and information for today, [CURRENT_DATE].

Consider the following:

• Calendar: [LIST_TODAY'S_MEETINGS_AND_EVENTS]
• Inbox Highlights: [SUMMARIZE_KEY_EMAILS_OR_ACTION_ITEMS]
• Top Priorities: [LIST_YOUR_TOP_3_PERSONAL_OR_WORK_PRIORITIES]
• Relevant News/Updates: [BRIEF_SUMMARY_OF_INDUSTRY_NEWS_OR_PROJECT_UPDATES]

Organize this information into a clear overview. Prioritize tasks and suggest concrete next steps for the most important items.

### 4. Codebase Migration Strategy with AI Agents
*Use this prompt to develop a high-level strategy for migrating a large codebase using AI agents, focusing on parallel processing and dependency management.*

You are an expert in large-scale software migrations and AI-assisted development. I need to migrate a [CURRENT_LANGUAGE/FRAMEWORK] codebase of approximately [NUMBER] lines of code to [TARGET_LANGUAGE/FRAMEWORK].

Outline a strategy for using multiple AI agents to handle this migration.

Consider the following aspects:

1. Code Analysis Agent: How would it identify dependencies, refactoring opportunities, and potential breaking changes?
2. Code Transformation Agent: How would it handle syntax conversion, API mapping, and idiom translation?
3. Testing Agent: How would it generate and execute tests to ensure functional equivalence?
4. Validation/Review Agent: How would it ensure the quality and correctness of the migrated code?
5. Orchestration: How would these agents work in parallel or sequence to manage the migration process across the entire codebase?

### 5. Financial Workflow Automation Plan
*Use this prompt to plan the automation of a specific financial workflow using AI agents, including data sources and output deliverables.*

You are a financial operations expert. I want to automate the "[FINANCIAL_WORKFLOW_NAME]" workflow.

This workflow involves: [DESCRIBE_CURRENT_MANUAL_STEPS_AND_DATA_SOURCES].

Propose a plan for using AI agents to automate this process.

For each step, identify:

• The specific task the AI agent would perform (e.g., data extraction, analysis, report generation).
• The required data sources (e.g., Dun & Bradstreet, internal databases, market feeds).
• The expected output or deliverable (e.g., pitch deck, market analysis report, verified business identity).
• Any necessary human oversight or approval points.

### 6. Design Concept Generation with Visual Elements
*Use this prompt to generate design concepts for a visual output, specifying elements, style, and target audience.*

You are a creative director and visual designer. I need design concepts for a [VISUAL_OUTPUT_TYPE, e.g., marketing slide deck, product prototype, one-pager] for [TARGET_AUDIENCE].

The core message is: "[MAIN_MESSAGE]".

Generate 3 distinct design concepts, each with:

1. A brief description of the overall aesthetic and mood.
2. Key visual elements and their placement (e.g., imagery, icons, data visualizations).
3. Color palette suggestions (3-4 colors).
4. Typography recommendations (e.g., modern sans-serif, elegant serif).
5. A rationale for why this concept resonates with the target audience and message.

### 7. Debugging with "Rubber Duck" Method
*Use this prompt to simulate a "rubber duck" debugging session, explaining your code and asking for potential issues or alternative approaches.*

You are an experienced software debugger. I'm currently working on a [LANGUAGE] program and encountering an unexpected issue. I've been trying to debug it, but I'm stuck.

Here's my code and a description of the problem:

Code:

**Problem Description:**
[EXPLAIN_THE_BUG_AND_WHAT_YOU'VE_TRIED]

Walk me through your thought process as if I were explaining this to you. Point out any potential logical errors, syntax issues, or common pitfalls you notice. Suggest alternative debugging steps or approaches I might not have considered.

Historical Session Analysis with Chronicle

Use this prompt to simulate a chronicle command, analyzing past AI interaction sessions to identify productivity patterns and insights.

You are an AI productivity analyst. I need to review my past AI interaction sessions to understand my work patterns and identify areas for improvement.
Assume I have access to a `/chronicle` command.
Based on the following hypothetical session logs:

**Session 1 (2026-06-03, 10:00 AM):** Brainstormed blog post ideas for "AI Agents." Used 15 prompts. Result: 5 solid ideas.
**Session 2 (2026-06-03, 2:00 PM):** Debugged a Python script. Used 10 prompts, 3 tool calls. Result: Bug fixed after 45 minutes.
**Session 3 (2026-06-04, 9:00 AM):** Drafted an email for a client. Used 7 prompts. Result: Email sent, positive feedback.
**Session 4 (2026-06-04, 3:00 PM):** Researched "quantum computing applications." Used 20 prompts, 5 web searches. Result: Comprehensive summary, but felt overwhelmed by information.

Provide a summary of my activity, identify any recurring themes or productivity bottlenecks, and offer 3 personalized tips to enhance my future AI usage based on these patterns.

Learning Plan for a New Topic

Use this prompt to create a personalized learning plan for a new topic, tailored to your current level and desired outcome.

I am at a [CURRENT_LEVEL, e.g., beginner, intermediate] level in [TOPIC]. My goal is to [SPECIFIC_OBJECTIVE, e.g., become proficient enough to build a basic app, understand core concepts for a job interview] within [TIMEFRAME, e.g., 3 months].
Create a weekly learning plan for me. Include:
- Specific sub-topics to cover each week.
- Recommended resources (e.g., "Intro to [TOPIC] by [AUTHOR]," "Online Course: [COURSE_NAME]," "Practical Project: Build a [PROJECT_TYPE]").
- Measurable milestones to track my progress.
- Suggestions for hands-on practice or exercises.

Ethical AI Deployment Checklist

Use this prompt to generate a checklist for deploying an AI model ethically and securely, incorporating recent concerns like cybersecurity safeguards.

You are an AI ethics and security consultant. I am about to deploy a new AI model, [MODEL_NAME], for [APPLICATION_DESCRIPTION].
Provide a comprehensive checklist of ethical and security considerations I must address before and during deployment.
Include points related to:
- Data privacy and bias.
- Transparency and explainability.
- Potential misuse (e.g., cybersecurity risks, misinformation).
- Robustness and reliability.
- User consent and control.
- Monitoring and auditing.
- Compliance with relevant regulations.
For each point, suggest a concrete action or mitigation strategy.