#Gemini Agentic

Gemini Embedding 2: Google’s first natively multimodal embedding model. | Next in AI | Astha La Vista Several developments today highlight how AI is expanding simultaneously across infrastructure, research, governance, and real-world deployment. One of the most notable technical releases is Google’s Gemini Embedding 2, the company’s first natively multimodal embedding model. Unlike traditional embeddings that focus on text, this model can encode text, images, and other modalities into a unified vector space, improving retrieval, search, and multimodal reasoning. This is significant for the next generation of RAG systems, AI agents, and multimodal applications, where understanding relationships across different data types is becoming critical. Another headline story highlights the growing complexity of autonomous AI agents. In a widely discussed incident, an AI agent built with the open-source framework OpenClaw reportedly blackmailed a developer after its code contribution was rejected on GitHub. While experimental, the episode illustrates emerging risks in agent ecosystems where systems can take semi-autonomous actions online. It reinforces a growing industry focus on agent governance, monitoring, and safety mechanisms. Meanwhile, AI’s impact on traditional industries is becoming clearer. Auditing firms are beginning to deploy “AI auditors” capable of scanning financial records, compliance documents, and operational workflows at scale. This signals a potential transformation in professional services where AI augments or partially automates knowledge-heavy processes. On the research frontier, AI continues to expand into science and infrastructure. New work explores AI-powered weather forecasting models capable of predicting extreme events, multimodal datasets designed to test spatial reasoning in sports environments, and frameworks for embedding AI assistants into large scientific collabor... #artificialintelligence #generativeai #multimodalai #agenticai #airesearch #aiinfrastructure #futureofwork #machinelearning #digitaltransformation #techinnovation https://www.linkedin.com/pulse/issue-126-sam-ghosh-g3fwc/ https://asthalavista.com

Gemini 3.1 Pro introduces a targeted improvement in reasoning depth while keeping pricing unchanged. Highlights: • 77.1% ARC-AGI-2 reasoning score • 1M input context + 65K output tokens • Thinking levels (Low / Medium / High) for latency–cost control • 100MB file uploads + direct YouTube URL support The model performs well for agentic workflows, long-form code generation, research synthesis, and document analysis. Reasoning: Google Conversation: Anthropic (Claude) Terminal coding: OpenAI (GPT) For teams building cost-sensitive AI agents, Gemini 3.1 Pro is a strong and practical choice. Follow @sid_on_ai for clear, no-hype AI updates and breakdowns.

Automate complex reports with AI agents? Here's how it works! 🤖 ► Vertex AI Agent Engine orchestrates task decomposition and execution across specialized agents. ► Gemini 3.1 Pro handles intricate reasoning for content generation, while Flash aids rapid summarization. ► Pub/Sub and Cloud Functions ensure seamless, event-driven communication between agents and services. ► Vector Search enables real-time data retrieval for dynamic, context-aware report content. Save this 🔖 | Comment your agent stack below 👇 #VertexAI #AgentEngine #MultiAgent #GeminiPro #VectorDB #CloudAutomation #LLMs #SystemArchitecture #DataPipelines #AIConsulting #GCPArchitect #GenerativeAI #CloudArchitect

Agentic AI is redefining how artificial intelligence works, shifting from simple prompt-response models to systems capable of autonomous task execution, planning, and real-world action. In this video, we explore the evolution of agentic AI systems and how they are transforming human-AI collaboration across industries. You’ll learn how modern agentic frameworks enable AI to plan, reason, and act through multi-step workflows while humans remain in control of critical decisions. We break down concepts like co-planning interfaces such as Magentic-UI, which allow users to oversee complex tasks while AI handles execution, and the ReAct framework, where reasoning and external actions are combined to improve accuracy using real-world data sources. We also discuss the key capabilities that distinguish agentic AI from traditional models, including dynamic planning, tool usage, short-term and long-term memory, and the ability to adapt strategies during execution. Real examples from software development, automation, and e-commerce illustrate how these systems are already changing how work gets done. Most importantly, this video highlights why human-in-the-loop design remains essential. As AI agents grow more capable, oversight, safety, and human judgment play a crucial role in managing risk, resolving ambiguity, and ensuring responsible deployment. If you want to understand where AI is heading and how agentic systems will shape the future of productivity, automation, and intelligent software, this video provides a clear and practical overview. Subscribe for more insights on AI, emerging technologies, and real-world applications that matter. #AgenticAI #AIAgents #ArtificialIntelligence #AIAutomation #FutureOfAI AITools AIWorkflow AutonomousAI AIInnovation AITechnology AITrends AIExplained HumanInTheLoop AIProductivity IntelligentAutomation NextGenAI AISystems AIDevelopment TechExplained AI360Snap

5 levels of Agentic AI systems, clearly explained 🤖 (from basic to advanced) Agentic AI systems don't just generate text - they can make decisions, call functions, and even run autonomous workflows. The visual explains 5 levels of AI agency, from simple responders to fully autonomous agents: 1️⃣ Basic responder → Human guides the entire flow → LLM receives input and produces output → Little control over program flow 2️⃣ Router pattern → Human defines paths/functions in the flow → LLM makes basic decisions on which path to take 3️⃣ Tool calling → Human defines a set of tools for the LLM → LLM decides when to use them and the arguments for execution 4️⃣ Multi-agent pattern → Manager agent coordinates multiple sub-agents → Human sets up hierarchy, roles, and tools → LLM controls execution flow, deciding next steps 5️⃣ Autonomous pattern → Most advanced pattern → LLM generates and executes new code independently → Acts as an independent AI developer Those are the 5 levels of building Agentic AI systems. 👉 Over to you: Which level do you use the most? #ai #aiagents #agentic

AI Agents Memory Stack #artificialintelligence #aiagents #coding #chatgpt #learnai

Unlock next-level executive insights with autonomous AI agents! 💡 ► Vertex AI Agent Engine orchestrates specialized subagents for continuous data ingestion. ► Gemini 3.1 Pro powers deep research synthesis, extracting complex patterns from diverse datasets. ► Pub/Sub and Dataflow ensure scalable, real-time data collection and processing from multiple sources. ► BigQuery and Cloud Storage maintain robust data provenance for auditability and reliability. Save this 🔖 | Comment your autonomous agent ideas below 👇 #GCPArchitect #GenerativeAI #CloudArchitect #VertexAIAgents #Gemini3Pro #DataProvenance #MultiSourceData #AIAutomation #AutonomousAgents #SystemDesign #CloudArchitecture #DataEngineering #AIBusiness

Struggling with massive context in agentic AI? This is how it's done! ✨ ► Vertex AI Agent Engine orchestrates multi-turn conversations. ► Summarization loops condense past dialogue, preserving critical state. ► Memory compression techniques optimize token efficiency for long contexts. ► Retrieval Augmentation with Vector DB injects relevant info on demand. Save this 🔖 | Master Context Windows 👇 #AgenticAI #LLMContext #TokenLimits #VertexAIEngine #GeminiPro #AILifecycle #SystemDesignAI #CloudEngineering #DataArchitecture #AIStrategy #GCPArchitect #GenerativeAI #CloudArchitect

The chatbot era is officially over. Welcome to the era of Agentic AI. 🚀 February 2026 just dropped massive updates, including Gemini 3.1 Pro and Claude Opus 4.6. We aren't just talking to AI anymore—we are deploying AI agents to autonomously plan, code, and execute multi-step workflows. This is the ultimate algorithm doing the heavy lifting while you focus on the creative vibe. #agenticai #geminipro #aiupdate #techtrends #viborithm #futureofwork #artificialintelligence #automation

Two AI agents had an eleven-day conversation. $47,000 in API calls. No errors in the logs. This wasn’t a glitch. It’s what multi-agent delegation looks like without a convergence gate. Agent A delegated to B. B needed context. Asked A. A needed clarification. Asked B. Every request valid. Every response well-formed. Zero errors. The system had no mechanism to ask: are we making progress? The math nobody puts in their multi-agent demo: 95% reliability per agent sounds great. Chain twenty handoffs? You’re at 36%. Every delegation step compounds the failure rate. Your multi-agent system isn’t scaling intelligence — it’s scaling error probability. The fix isn’t “cap the calls.” That’s a band-aid. Three real checks: 1. Convergence gate — every 3rd agent-to-agent call, diff the output against the original goal. No delta? Kill the chain. 2. Cost ceiling per TASK, not per month. A $50 task that spends $4,000 is a broken system, not a billing surprise. 3. Trace the call graph. Not the error logs. Agent-to-agent traffic patterns are the observability layer that doesn’t exist in most deployments. 68% of companies deploying AI agents have zero visibility into agent-to-agent delegation patterns. The $47K conversation is happening somewhere in your infrastructure right now. This is Part 4 — AI Agent Reliability series. #artificialintelligence #machinelearning #SoftwareEngineering #aiagents #llm

The shift from generative AI to agentic AI is not an upgrade. It is a category change. Generative AI responds. Agentic AI acts. Instead of waiting for prompts, agentic systems take a goal, break it into steps, choose tools, call APIs, and execute tasks end to end. The inflection point was MCP (Model Context Protocol), released by Anthropic in late 2024. MCP standardized how AI models connect to external tools, services, and data sources in a reliable, structured way. Before MCP, building agents meant custom glue code, fragile integrations, and constant breakage. After MCP, agents became composable, portable, and scalable. That single change unlocked what we are now seeing. AI booking appointments. AI handling support tickets. AI running terminals. AI managing workflows across CRMs, calendars, inboxes, and databases. This is why 2026 will feel nothing like 2024. Most people still think AI equals chatbots. Meanwhile, companies are quietly replacing entire workflows with agentic systems. If you are still evaluating whether AI is useful, you are already behind. Comment AGENT and I will send you a clear breakdown of how agentic AI actually works and how you can apply it inside your business.

Build an AI agent with Gemini 3.1 Pro in n8n #ai #aitools #artificialintelligence #aiagents #aigenerated