Core Insights - Microsoft has introduced a new tool named Agent 365 that allows IT specialists to manage artificial intelligence agents within corporate systems, enhancing control and visibility over these agents [2][4]. Group 1: Product Features - Agent 365 provides a comprehensive list of AI agents operating within a company's systems, including those from third-party vendors [2]. - Administrators can approve new agents, monitor their popularity, and assess the amount of employee time saved weekly [4]. - The software also enables the identification of security risks associated with agents, allowing for blocking of potentially harmful agents [4]. Group 2: Industry Context - The rise of AI agents has been significantly influenced by the popularity of OpenAI's ChatGPT, prompting traditional corporations to adopt these technologies for various tasks [5]. - Major software companies, including Adobe and ServiceNow, will have their agents automatically integrated into Agent 365, showcasing a collaborative ecosystem [6]. - EY, a leading accounting firm, has transitioned to using Agent 365 for better control over its internal AI agents, indicating a trend among corporations to seek enhanced management tools [7]. Group 3: Market Engagement - Customers in Microsoft's Frontier program can request early access to Agent 365, highlighting the company's strategy to engage with early adopters [8]. - Microsoft has not yet finalized pricing for Agent 365, suggesting ongoing development and market positioning efforts [8].

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Financial Performance - Gross revenue increased from $0.25 million to $0.69 million for the three months ended September 30, 2025 [35] - Net revenue increased from $0.60 million to $1.63 million for the nine months ended September 30, 2025 [35] - Net loss was $5.2 million for the three months ended September 30, 2025 [35] - Total Remaining Performance Obligation (RPO) decreased from $14.3 million to $3.6 million [36] - Booked Annual Recurring Revenue (bARR) was $2.35 million for the nine months ended September 30, 2025 [37] Business Highlights - Q3 contract bookings included a top 10 global supermarket retailer and NEC Networks & System Integration Corp [9] - The company is engaged with prospects ranked in the top 10 leaders in their industries [13] - authID's ID Verification and Privacy-Preserving Biometrics are used for NESIC's Symphonict Trust Platform [16] - authID is powering Prove's Unified fraud platform with PrivacyKey's 1:1Billion signal [16] - authID co-developed and launched IDProof+, a High Assurance Identity Verification solution combining AuthID's Proof with Microsoft Entra Verified ID [16] Strategic Positioning - The company is positioned to capitalize on the market shift to biometrics due to increasing fraud and the rise of AI [29, 32] - authID is innovating to solve for enterprise adoption by integrating privacy-preserving biometrics [17] - The company is focused on securing new customer contracts, establishing contractual commitments, recognizing revenue, and retaining/expanding customer relationships [38]

Context Engineering Principles for AI Agents - The industry recognizes the increasing task length AI agents can perform, with task length doubling approximately every seven months [2] - The industry faces challenges related to context rot, where performance degrades with longer context lengths, impacting cost and latency [3][4] - Context engineering, involving offloading, reducing, and isolating context, is crucial for managing context rot in AI agents [8][9][10] Context Offloading - Giving agents access to a file system is beneficial for saving and recalling information during long-running tasks and across different agent invocations [11][15][18] - Offloading actions from tools to scripts in a file system expands the agent's action space while minimizing the number of tools and instructions [19][22] - Progressive disclosure of actions, such as with Claude skills, saves tokens by selectively loading skill information only when needed [26][30] Context Reduction - Compaction, summarization, and filtering are techniques used to reduce context size and prevent excessively large tool results from being passed to the language model [32][33][39] - Manis compacts old tool results by saving them to a file and referencing the file in the message history [34] - Deep agents package applies summarization after a threshold of 170,000 tokens [38] Context Isolation - Context isolation, using separate context windows or sub-agents for individual tasks, helps manage context and improve performance [10][39][40] - Sub-agents can have shared context with the parent agent, such as access to the same file system [42] Tool Usage - Agent harnesses often employ a minimal number of general, atomic tools to save tokens and minimize decision-making complexity [44] - Cloud code uses around a dozen tools, Manis uses less than 20, and the deep agent CLI uses 11 [24][25][44]

Market Trends & Adoption - x402 processes 720 thousand daily transactions, with 98% occurring on the Base chain [1] - AI agents are already processing $3 million USDC volume [1] - PayAI Network, an x402 facilitator, is pulling $136 million in daily volume [1] Base Ecosystem & Valuation - JPMorgan values Base at $34 billion, despite it not having a token yet [1] - Base owns the payment infrastructure needed for AI agents [1] Infrastructure Development - Coinbase and Cloudflare have built payment rails for AI agents [1] Investment Opportunity - Potential airdrop farming opportunities exist through x402 facilitators [1]

Financial Highlights - Total revenue reached $402.2 million, a 27% year-over-year increase[15] - Adjusted EBITDA was $29 million, a 241% year-over-year increase[15] - The company holds $64 million in cash[15] - The company has $0 debt[15] Strategic Initiatives - Bakkt is focusing on stablecoins and Real-World Asset (RWA) tokenization for growth[11] - The company is expanding its technology and services to new markets through Bakkt Global[23] - Bakkt is developing Bakkt Agent, an AI-powered programmable finance platform[19] Market Opportunity - The total stablecoin market cap is $308 billion as of October 24, 2025[11] - The stablecoin market is projected to have a 38% Compound Annual Growth Rate (CAGR) through 2030[11] - Estimated growth in tokenization through 2033 is $46 trillion[11]

New Platform Launch - Virtuals launches Luna dot Fun, the first meme launchpad with AIGC (AI Generated Content) [1] Token Functionality - Every token on Luna will power its own content engine [1] AI Agent Economy - AI Agents produce content and can earn revenue onchain via a fully agentic economy [1]

x402 = a new payment gateway for AI agents.Click → sign → verify → boom, paid in crypto.No banks. No API keys. Just one line of code. AI agents finally have their own money rail. https://t.co/SiG2KDGASR ...

DeAI & Robotics Developments - Agentic registry (ERC-8004) and x402 gain traction as the new economic system for agents [1] - Jesse Pollak launches an AI agent to assist builders [1] - Warden Protocol reveals Kaibot 2.0 for real-time X intelligence and crypto portfolio management [1] - DefiLlama introduces LlamaAI, an AI tool for deep analysis and chart creation [1] - Solana opens x402 Hackathon with $100 thousand in prizes [1] - Virtuals.io rolls out Agentic Micropayments and agentic Fund of Funds [1] Token & Ecosystem Updates - Messari Crypto reports GEODNET's 217% year-over-year growth and 59% quarter-over-quarter token burn [1] - GTA Ventures enables wrapping alpha tokens natively within the Bittensor ecosystem and leveraging CCIP to bring it to Base [1] - TAO Synergies Inc (NASDAQ: TAOX) announces a $750 thousand initial investment in Yuma Group's Bittensor subnet funds, split evenly between two funds ($375 thousand each) [1] - Chutes SN64 Alpha Token will be listed on MEXC, broadening TAO ecosystem exposure [1] - Donut Browser raised $22 million to advance agentic crypto browsing for trading [2]

RT Avi Chawla (@_avichawla)5 Agentic AI design patterns, explained visually!Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration!The visual depicts the 5 most popular design patterns for building AI Agents.1️⃣ Reflection patternThe AI reviews its own work to spot mistakes and iterate until it produces the final response.2️⃣ Tool use patternTools allow LLMs to gather more information by:- Querying a vector database- Executing Python scripts- Invoki ...