If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Google did it again!First, they launched ADK, a fully open-source framework to build, orchestrate, evaluate, and deploy production-grade Agentic systems.And now, they have made it even powerful!Google ADK is now fully compatible with all three major AI protocols out there: https://t.co/nMkcyLEhyl ...

Google did it again!First, they launched ADK, a fully open-source framework to build, orchestrate, evaluate, and deploy production-grade Agentic systems.And now, they have made it even powerful!Google ADK is now fully compatible with all three major AI protocols out there:- MCP: To connect to external tools- A2A: To connect to other agents- AG-UI: To connect to users.AG-UI is the newest addition, which is an open-source protocol that enables agents to collaborate with users.They worked with the AG-UI team t ...

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]