This is the report out of MIT that absolutely shook the AI industry. It said 95% of AI pilots inside the enterprise fail. And this report went viral.It was everywhere just a couple months ago. Fast forward just a few months later and today AWS is hosting reinvent their annual massive conference and they just made two announcements to their agentic platform that really look to solve the issues that enterprises are having deploying agentic systems into production. Those two big problems how to trust AI and ho ...

AI Agentic Systems - Google launched ADK, a fully open-source framework for building, orchestrating, evaluating, and deploying production-grade Agentic systems [1] - Google ADK is now fully compatible with all three major AI protocols [1]

Product Launch & Features - Google launched ADK, an open-source framework for building, orchestrating, evaluating, and deploying Agentic systems [1] - Google ADK is now compatible with MCP (for connecting to external tools), A2A (for connecting to other agents), and AG-UI (for connecting to users) [1] - AG-UI is a new open-source protocol enabling agents to collaborate with users [1] Integration & Development - AG-UI facilitates a bridge between backend AI agents and full-stack applications [2] - CopilotKit provides building blocks to integrate agents into frontend applications [2] - Connecting an agent to a React frontend using CopilotKit involves defining the agent with ADK and connecting it [2]

Core Concepts - LLMs like GPT and DeepSeek serve as the foundational engine powering Agentic AI [1] - AI Agents wrap around LLMs, granting them autonomous action capabilities and making them useful in real-world workflows [2] - Agentic systems emerge from combining multiple agents, enabling collaboration and coordination [3] Agentic Infrastructure - Agentic Infrastructure encompasses tokenization & inference parameters, prompt engineering, and LLM APIs [2] - Tool usage & function calling, agent reasoning (e g, ReAct), task planning & decomposition, and memory management are crucial components [3] - Inter-Agent communication, routing & scheduling, state coordination, and Multi-Agent RAG facilitate collaboration [4] - Agent roles & specialization and orchestration frameworks (e g, CrewAI) enhance workflow construction [4] Trust, Safety, and Scalability - Observability & logging (e g, using DeepEval), error handling & retries, and security & access control are essential for trust and safety [6] - Rate limiting & cost management, workflow automation, and human-in-the-loop controls ensure scalability and governance [6] - Agentic AI features a stacked architecture, with outer layers adding reliability, coordination, and governance [5]

Core Concepts - LLMs like GPT and DeepSeek power Agentic AI [1] - AI Agents wrap around LLMs, enabling autonomous action [2] - Agentic systems combine multiple agents for collaboration [2] Agentic Infrastructure - Observability & logging track performance using frameworks like DeepEval [2] - Tokenization & inference parameters define text processing [3] - Prompt engineering improves output quality [3] - Tool usage & function calling connect LLMs to external APIs [4] - Agent reasoning methods include ReAct and Chain-of-Thought [4] - Task planning & decomposition break down large tasks [4] - Memory management tracks history and context [4] Multi-Agent Systems - Inter-Agent communication uses protocols like ACP, A2A [5] - Routing & scheduling determines agent task allocation [5] - State coordination ensures consistency in collaboration [5] - Multi-Agent RAG uses retrieval-augmented generation [5] - Orchestration frameworks like CrewAI build workflows [5] Enterprise Considerations - Error handling & retries provide resilience [7] - Security & access control prevent overreach [7] - Rate limiting & cost management control resource usage [7] - Human-in-the-loop controls allow oversight [7]

Core Argument - Factory believes agentic systems will radically change software development, transitioning from human-driven to agent-driven development [2] - The company emphasizes that AI tools are only as good as the context they receive, and providing comprehensive context is crucial for effective AI-assisted development [14][15][16] - The company advocates for using agents at every stage of development, including planning and design, by delegating groundwork and research to AI agents [18][19][20] Technological Advancements - The company's "droids" can ingest tasks, ground themselves in the environment, search codebases, and generate pull requests that pass CI [12][13] - The company's platform integrates natively with various data sources, enabling agents to access and utilize information from across the organization [17] - The company's system can condense incident response search efforts from hours to minutes by pulling context from relevant system logs, past incidents, and team discussions [31][32] Enterprise Solutions & Security - Factory is an enterprise platform focused on security, auditability, and ownership concerns related to AI agents in large organizations [41][42] - The company offers a platform with controls to address security concerns and emphasizes the importance of responsible AI implementation within enterprises [43] - The company provides 20 million free tokens for users to try out the droids [40] Future of Software Development - The industry is moving from executing to orchestrating systems, with developers managing agents and building patterns that supersede the inner loop of software development [27][38] - The future belongs to developers who can effectively work with AI agents, with clear communication skills being paramount [39] - AI agents amplify individual capabilities, allowing developers to focus on higher-leverage tasks and the outer loop of software development [37][38]