Anthropic launches the highly powerful Claude Fable 5 model

Thariq Shihipar's post from the Claude Code team at Anthropic has drawn significant attention in the AI user community. He revealed Dynamic Workflows, a feature that allows Claude to design its own workflows instead of just waiting for commands, and this is considered the most important upgrade since Claude Code gained skills and subagents. This feature uses the harness concept as its foundation to handle technical requirements. Three fatal errors that cause AI agents to fail at complex tasks Before discussing the solution, Thariq points out an uncomfortable reality: most AI agents today face serious problems when handling complex, multi-step tasks within a single context window. He categorizes them into three core failure modes that nearly every agent system encounters. Agentic laziness: when AI declares done after finishing only half the work This is the phenomenon of Agentic Laziness, where an agent completes part of the work and then self-reports as finished. A specific example: you ask an agent to review 50 code files, but it only looks through 20 files and concludes that everything is fine. The cause lies in context window limitations, and when the amount of information is too large, the agent tends to take shortcuts to finish faster. Will an agent be biased toward itself? An agent being biased toward itself is called Self-Preferential Bias, and this occurs when you ask an agent to review its own results. Like asking a student to grade their own exam, the agent tends to favor the results it already produced, leading to uncritical validation and overlooking potential errors. This is particularly dangerous in tasks requiring high accuracy. How to prevent an agent from losing its original intent step by step Goal Drift is the phenomenon where an agent gradually forgets its original goal after many processing steps or after context compaction. Specific constraints like "don't do X" or important edge cases can be dropped when memory is summarized, so the final result deviates from the original requirement without the agent ever realizing it. Dynamic Workflows helps Claude write its own work orchestration framework Anthropic's solution is not to make the model smarter, but to change how Claude organizes work. Dynamic Workflows transforms Claude from a code-writing agent into an agent that designs operational workflows for complex tasks. The core concept here is self-organization: Claude can analyze goals on its own, choose the appropriate working mode, and create an internal workflow before starting execution. Custom harness instead of a fixed workflow Instead of operating within a fixed environment, Claude writes a harness framework in JavaScript designed specifically for each task. This harness acts like a project manager: it breaks down the work, initializes specialized sub-agents for each part, assigns appropriate tools, routes work to different models, and performs adversarial verification to ensure quality. How does a harness work? To understand more clearly, imagine the harness as a theatrical script that Claude writes for itself before performing. When given a complex task, Claude does not dive straight in but pauses to write a JavaScript snippet describing the entire workflow: how many sub-agents are needed, what each agent does, what order things happen in, and how results from one agent are passed to the next. A concrete example: if you ask Claude to audit 1,000 Slack messages to find recurring incidents, the harness might look like this logically: Agent 1 (classification): reads all messages and assigns labels by topic Agent 2, 3, 4 (parallel processing): each agent deeply analyzes one topic group Agent 5 (synthesis): collects results from the three agents above and removes duplicates Agent 6 (cross-check): re-reads the synthesized results and provides independent critique The important point is that Claude writes this harness based on the specific characteristics of each task, not according to a rigid template. Different tasks produce different harnesses, and that is exactly why this feature is called "dynamic." The harness is written in JavaScript and runs within the Claude Code environment. You can activate Dynamic Workflows by saying "use a workflow," however this phrase is easily confused with regular workflows, so it is recommended to use the keyword "ultracode" in your prompt to clearly distinguish between a regular workflow and a Dynamic Workflow and save more tokens. Context isolation to prevent context degradation One of the smartest design choices in Dynamic Workflows is the Isolation feature. Each sub-agent is given its own separate context window, completely independent from other agents. This prevents the phenomenon of context rot, meaning the quality degradation that occurs when a context window becomes overloaded, while also eliminating both Agentic Laziness and Goal Drift since each agent focuses only on its assigned piece of work. Six reusable orchestration patterns Claude can combine six available orchestration patterns to handle a wide variety of situations: Classify and act: classifies input then selects the appropriate action Fan out and synthesize: splits work into multiple parallel branches then synthesizes the results Cross-check verification: uses a separate agent to cross-check results Generate and filter: generates multiple options then filters for the best one Tournament: puts options into direct head-to-head elimination rounds Loop until done: repeats until a quality threshold is reached Can you optimize costs when using Dynamic Workflows? Running multiple sub-agents in parallel might sound expensive, but Dynamic Workflows is actually designed to optimize costs in several specific ways. Smart routing to the right model Not every step in a workflow needs the most powerful model. The harness allows Claude to route each task to a model that matches its complexity: simple classification steps can run on smaller, cheaper models, while only steps requiring deep reasoning need a large model. The result is that total costs are often lower than running the entire workflow on a single model. Context isolation helps reduce token consumption Because each sub-agent only receives the portion of context it actually needs for its work, total token consumption across the entire workflow is often significantly lower compared to the traditional approach, where the full conversation history gets stuffed into a single context window that keeps growing larger. Avoiding rework through early checkpoints The harness can install quality checkpoints between steps. If a step produces a result that does not meet requirements, the system stops and reprocesses just that step rather than running the entire workflow to completion before discovering an error at the end. This approach saves significant costs for long multi-step tasks. If you are concerned about costs, start with moderate-volume tasks to observe actual token consumption before scaling up. What are the real-world applications of Dynamic Workflows? What excites Thariq most is not the coding capability, but the way Dynamic Workflows extends Claude Code into non-technical tasks. The feature can be activated with natural language (for example: "use a workflow") or the keyword "ultracode." Real-world applications include: Auditing thousands of Slack messages to find recurring incidents Systematically ranking and screening large candidate pools Running automated live elimination tournaments to choose the best name for a CLI tool Handling high-precision operational tasks that previously only humans could perform The design philosophy is architectural constraints rather than raw intelligence The most notable aspect of Anthropic's approach is the design philosophy: rather than trying to increase the raw intelligence of the model, they build architectural constraints into the workflow. In other words, instead of hoping the model will naturally know how to avoid mistakes, they design the system so that errors are hard to occur in the first place, and the harness is the tool that enforces that philosophy. Dynamic Workflows shows that the next step forward for AI agents does not lie in smarter models but in the ability to design workflows on their own. Just as a good manager divides work among a team rather than doing everything alone, Claude can now organize its own team of sub-agents, and this is a clear signal that the future of AI coding is no longer just about writing code faster but about organizing work better.

Imagine having an AI assistant that is incredibly smart but forgets everything between sessions and cannot check the quality of its own work. To solve this problem, developers created a protective management layer around AI models called an agent harness. This is what enables AI agents to complete complex, multi-step tasks autonomously without requiring constant human intervention. What is an agent harness? Think of an AI model as a brilliant new employee with no long-term memory and zero familiarity with the workplace. They can solve complex problems in seconds but will just as easily forget what they were working on, or accidentally send a confidential document to the wrong client. In that scenario, an agent harness acts as the experienced manager sitting right beside them, keeping things on track. Put simply, an agent harness is the software layer wrapping around an AI model that handles all administrative and logistical work so the model itself can focus entirely on reasoning and problem-solving. It connects the AI to external tools, maintains a complete record of work across sessions, and verifies results before considering a task done. In practice, an agent harness handles the following: Connecting the AI model to external tools such as web search, email, and calendars Persisting progress across sessions so the AI never has to start from scratch Filtering out irrelevant information and supplying only the data the AI actually needs at each step Monitoring AI actions to prevent dangerous mistakes Logging activity in detail so humans can audit what happened when needed Origin of the term: The concept of "agent harness" was formally named by technology engineer Mitchell Hashimoto in early 2026. Before that, many development teams had built similar systems but had no shared term for this layer of infrastructure. Why do AI agents fail at long-running tasks? The biggest weakness in today's AI models is the complete absence of long-term memory. Every new conversation starts from zero with no recollection of anything that happened before. Imagine hiring an employee who wakes up every morning having forgotten every agreement, every deadline, and every piece of progress from the day before. When Anthropic tested Claude building a complex web application without harness support, the results were consistently disappointing. Two failure modes kept appearing: The AI tried to do everything at once, ran out of working memory midway through, and left the project unfinished. The next session wasted time trying to figure out what had already been done. The AI declared the task complete without actually running the result to verify it worked. Beyond those two core failures, long-horizon tasks expose three additional problems: Context clog: Accumulated conversation history and tool outputs crowd out the original instructions, causing the AI to gradually lose focus on the actual goal Tool misuse: The AI sometimes searches for information that does not exist or submits incorrect inputs to forms, and without anything to stop it, repeats the same error in a loop Total progress loss on failure: Any network error or system crash wipes out whatever was stored in temporary memory, forcing a full restart Stanford research (2023): AI models tend to overlook information buried in the middle of long text, even when that text is not particularly long. This is why feeding too much data to an AI all at once often backfires without a filtering layer in place. How does an agent harness work in practice? An agent harness operates in two distinct phases to keep work flowing continuously without interruption. Setup phase (runs once) The harness prepares the full working environment before the AI begins: building a structured task list, initializing storage, and recording the starting point. Think of it as the manager drawing up a detailed project plan before handing anything off. This phase only needs to happen once. Execution phase (repeats) Each time the AI begins a new session, the harness automatically reloads all saved progress and assigns only the next relevant task. When the AI wants to take an action such as searching for information or sending a notification, the harness checks whether that request is valid, executes it safely, cleans the returned result, and passes it back to the AI. The model never touches external systems directly without going through this control layer first. The four core components of an agent harness For an AI to operate reliably over extended periods, a standard agent harness needs four essential components: External tool gateway: Allows the AI to interact with the real world by reading documents, searching the web, or sending messages. The harness acts as an intermediary, validating every request before execution and ensuring returned results are clean and usable. Layered memory management: Maintains three types of memory serving different needs: short-term working memory for the current session, a task log recording what has been completed and what remains, and a long-term knowledge store that accumulates across multiple projects over time. Intelligent context filter: Summarizes long conversation histories down to key points and supplies only the data relevant to the current step rather than loading everything at once, keeping the AI focused on the right task at the right moment. Safety checker and human approval gate: Automatically verifies results before marking a task as complete. For sensitive actions such as deleting important data or sending bulk emails, the harness pauses and waits for human confirmation before proceeding. Note on accumulated knowledge: If an AI agent's memory is stored entirely within a closed third-party platform, all the knowledge it builds up over time belongs to that platform. Switching to a different system means starting from zero. This is worth thinking through carefully when choosing a long-term AI agent solution. Harness engineering and the secret behind millions of lines of code Harness engineering is the practice of treating every AI failure as a system problem to fix permanently rather than something to retry or ignore. As Mitchell Hashimoto put it: if the agent makes a mistake, redesign the environment so that mistake becomes physically impossible to repeat. In practice, when OpenAI built large software projects with three engineers producing 3.5 pull requests each per day without typing a single line of code, they had set up automatic verification checks after every AI action. When the AI produced something incorrect, the system returned error messages written in a specific structure so the AI immediately understood what needed to change on the next attempt. Every error message became a learning signal, not just a warning. A study presented at ICML 2025 further confirmed that the same AI model equipped with a harness consistently outperformed itself running without one, even with identical training weights and identical prompts. The environment surrounding the AI matters just as much as the model itself. A telling data point: Anthropic's Claude Code has grown past 512,000 lines of code and continues to expand. More capable models do not make the harness simpler. They make it larger, because there is more capability to orchestrate and more failure modes to guard against. When do you actually need an agent harness? For simple one-off tasks like summarizing a document or answering a specific question, calling an AI directly is perfectly fine. But the moment work extends beyond a single conversation, requires memory from a previous session, or involves multiple steps that need to happen in a specific order, a harness becomes necessary. One thing worth reflecting on: the built-in web search in ChatGPT and Gemini is itself a form of harness. When AI automatically looks something up, there is infrastructure behind the scenes making the tool call, processing the result, and feeding clean information back into context. The harness is invisible to the user but indispensable to the system. Agent harness is not a short-term technical trend. It is the answer to fundamental limitations that AI cannot resolve on its own: no long-term memory, finite working context, and a tendency to misuse external tools without guardrails. 4AIVN has also started applying harness to our own workflows — and what we have found is that it does not just help AI finish tasks. It turns AI into a system that learns from failure and gets more reliable over time.

Microsoft just dropped seven new AI models at Build 2026, with MAI-Thinking-1 boasting 35 billion active parameters and trained entirely on clean data. For the first time, the software giant is openly challenging the position of its own strategic partner, OpenAI, on the AI model battlefield. MAI-Thinking-1 and Microsoft's reasoning ambitions The centerpiece of Build 2026 was MAI-Thinking-1, Microsoft's first reasoning AI model developed entirely in-house. With approximately 35 billion active parameters, the model is designed to handle multi-step reasoning tasks, work with long contexts, and support complex coding, all at a lower cost than many large-scale AI models currently available. The most notable claim is that Microsoft trained MAI-Thinking-1 on clean data without using distillation from third-party AI models. In other words, this is a clear statement that Microsoft has the independent AI research capability to build competitive models without "borrowing" knowledge from GPT or any other model. According to Microsoft's published evaluations, MAI-Thinking-1 achieves competitive performance on coding benchmarks and is rated on par with many leading AI models in blind evaluation tests. The 35-billion parameter count also signals that Microsoft is prioritizing efficiency over raw scale, as many competitor models have significantly more parameters but may not necessarily deliver better output quality. From coding to voice: a complete AI ecosystem Beyond reasoning, Microsoft introduced six additional AI models to build a complete AI ecosystem serving both individual users and enterprises. From coding and image generation to voice synthesis, every piece of the puzzle now has a dedicated model. Smarter coding with MAI-Code-1-Flash For developers, MAI-Code-1-Flash is significant news. This model specializes in code generation and software development support, optimized for real-world programming tasks. More importantly, it will be integrated directly into GitHub Copilot and Visual Studio Code, two tools used daily by millions of developers. This means code suggestions and automated coding experiences will be significantly upgraded within familiar development environments. Images and voice: the missing pieces In the creative content space, Microsoft announced MAI-Image-2.5 alongside MAI-Image-2.5-Flash. These are next-generation image creation and editing models, with the Flash version optimized for fast response times, making it suitable for real-time applications like live photo editing or on-demand illustration generation. In the audio domain, Microsoft introduced two important models: MAI-Voice-2 with more natural voice synthesis capabilities and support for additional languages MAI-Transcribe-1.5 for speech-to-text conversion with significantly faster processing speeds than the previous generation Additionally, Microsoft has developed optimized variants specifically for the Microsoft Foundry platform, helping enterprises easily build and deploy their own AI applications. The strategy to reduce OpenAI dependence Where Microsoft was previously seen mainly as an infrastructure partner and deployment platform for OpenAI, Build 2026 shows the company is steadily acquiring all the essential components of a full AI ecosystem. Microsoft now has its own reasoning model, coding model, image generation model, voice synthesis model, and speech recognition model, all connected directly to the Azure, Copilot, and Microsoft Foundry ecosystem. This strategy gives Microsoft greater autonomy in developing core technology while reducing risk from dependence on external partners. More specifically, owning proprietary AI models allows Microsoft to control its product roadmap, optimize operational costs, and customize models for specific service needs without waiting for or negotiating with third parties. Where does the AI model race go from here? The simultaneous launch of seven new AI models shows Microsoft is investing heavily in foundational technologies to compete directly with major players like OpenAI, Google, and Anthropic. When OpenAI's largest partner decides to build its own AI models, that is the clearest signal that the AI race has entered a new phase where no one wants to place the future of their technology in someone else's hands. For developers and enterprises, now is the time to closely watch Microsoft Foundry and the Azure AI ecosystem, as tools that were previously only available through OpenAI will soon appear within Microsoft's familiar ecosystem. Build 2026 may well be remembered as the moment Microsoft officially declared its vision for an independent, comprehensive AI ecosystem with its own distinctive identity.

Many people still do research manually: opening a dozen tabs, watching videos, reading articles, taking notes in scattered places, and then spending even more time trying to synthesize the result. A long-form post by monokern on X suggests a different pattern: use Claude Code to orchestrate the workflow, NotebookLM to analyze sources, and Obsidian to store long-term memory. Done correctly, this is not just a search session. It becomes an AI workflow that compounds over time. The core idea is practical: Claude Code does not need to do everything inside an expensive context window. It can call tools, run skills, create files, and offload heavy source processing to NotebookLM. The output is then saved back into Obsidian as markdown, giving the next research session better context. According to the original post, the initial setup can be completed in under 30 minutes if the required tools are already available. Why does this stack work? The strength of the workflow is that each tool owns a clear layer. Claude Code acts as the execution engine: it receives plain-language instructions, calls skills, runs commands, manages files, and coordinates the pipeline. Instead of forcing the user to operate each step manually, Claude Code becomes the system operator. NotebookLM is the analysis layer. Google's research tool can read sources, summarize them, generate analysis, flashcards, mindmaps, infographics, or audio overviews. When Claude Code sends source processing to NotebookLM, the user benefits from Google's processing layer rather than spending Claude tokens on every piece of long-form digestion. Obsidian is the memory layer. Every analysis result is saved as markdown in a personal vault. Over time, that vault becomes a structured knowledge base of topics, sources, observations, patterns, and conclusions. Claude Code can read those files later to understand what the user cares about, what formats they prefer, and how they tend to evaluate a topic. Skill Creator turns the workflow into a reusable tool The first major step in the guide is installing Skill Creator inside Claude Code. This layer lets users describe a new capability in natural language, after which Claude Code creates the skill structure, installs it, and makes it available as a reusable command. In other words, instead of rebuilding the research prompt every time, the user packages the workflow as a dedicated skill. The first example is a YouTube search skill. It uses yt-dlp to search videos by query and return metadata such as title, channel, views, duration, upload date, URL, and a views-to-subscribers ratio. For content or market research, this is more useful than a plain list of links because it shows which sources are actually attracting attention. NotebookLM handles the heavy analysis The post proposes connecting Claude Code to NotebookLM through notebooklm-py because NotebookLM does not currently provide an official public API. After installation and Google account authentication, Claude Code can use a custom skill to create a new notebook, add sources such as YouTube URLs, text, or files, and then ask NotebookLM to generate analysis or deliverables. The key point is that NotebookLM is not only a summarizer. In a real research pipeline, it can receive 10 videos on a topic, analyze which frameworks are gaining traction, which ones are overhyped, where the community disagrees, and what content gaps remain uncovered. That processing takes time, but most of the work happens on the NotebookLM side. The full pipeline: one command for a complete research task Once the YouTube search skill and NotebookLM skill exist, the next step is to create a pipeline skill that combines both. The user gives a topic, such as researching AI agent frameworks in 2026, and the pipeline searches for relevant sources, creates a notebook, adds those sources, runs the analysis, and returns the result as markdown. In monokern's example, the pipeline finds 10 video sources, sends them into NotebookLM, generates analysis, creates an infographic, and saves the result into Obsidian. The total processing time is described as around 6 minutes, most of which is NotebookLM processing. The practical value is that the user does not need to open every tab, copy every link, or manually combine the metadata. The final output is more than a chat answer. It includes full analysis, source lists, engagement metrics, trend observations, a visual deliverable, and a markdown file saved into the vault. That is what separates this workflow from a normal chatbot interaction. Obsidian makes the system smarter over time Obsidian is the most interesting part. If the workflow runs only once, it already saves time. But if it runs regularly, every new markdown file makes the personal knowledge base richer. After a month, Claude Code can see recurring topics, the types of insights the user values, and the preferred format for results. The post also highlights the role of the claude.md file inside the vault. This can become a configuration file describing working conventions, analysis style, and output preferences. After several research sessions, the user can ask Claude Code to read recent work and update that file so it better reflects the user's current process. The real value is the structure, not YouTube YouTube is only the data source in the example. The pipeline structure is the valuable part. Users can replace YouTube with academic PDFs, industry reports, public documentation, web pages, local files, transcripts, or Google Drive documents. As long as Claude Code can access the source and pass it into the analysis layer, the operational template stays the same. This opens many practical uses: researching a crypto ecosystem through whitepapers and public documentation, analyzing an emerging technology through conference talks, mapping content gaps in a niche, or tracking market dynamics from public reports. In every case, the same three layers remain: collect sources, analyze them, and store knowledge. What should you watch out for? This workflow is powerful, but it is not for everyone. It assumes the user is comfortable with Claude Code, has an Obsidian vault, can install CLI tools such as yt-dlp, and is willing to use an unofficial library to connect to NotebookLM. Also, because NotebookLM and YouTube can change access patterns, these skills should be treated as maintained tools rather than install-and-forget automation. Still, the underlying idea is important: instead of using AI as a disconnected chat box, turn it into a research system with memory, a pipeline, and the ability to learn from your own work history. For people who regularly analyze markets, technology, or content, this is far more practical than opening 10 tabs and manually stitching everything together.