ibl.ai Agentic AI Blog

Insights on building and deploying agentic AI systems. Our blog covers AI agent architectures, LLM infrastructure, MCP servers, enterprise deployment strategies, and real-world implementation guides. Whether you are a developer building AI agents, a CTO evaluating agentic platforms, or a technical leader driving AI adoption, you will find practical guidance here.

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We analyze key research from leading institutions and labs including Google DeepMind, Anthropic, OpenAI, Meta AI, McKinsey, and the World Economic Forum. Our content includes detailed analysis of reports on AI agents, foundation models, and enterprise AI strategy.

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CTOs, engineering leads, and AI architects turn to our blog for guidance on agent orchestration, model evaluation, infrastructure planning, and building production-ready AI systems. We provide frameworks for responsible AI deployment that balance capability with safety and reliability.

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Google: Agents – Architecture, Tools, and Applications

Jeremy WeaverJanuary 6, 2025
Premium

Generative AI agents extend language models by using external tools and orchestrated reasoning frameworks like ReAct and Chain-of-Thought, with practical implementations shown through examples such as LangChain and Vertex AI.

Google: Agents – Architecture, Tools, and Applications



Summary of Read Full Report

This whitepaper explains Generative AI agents, programs extending the capabilities of language models. Agents achieve goals by using tools (Extensions, Functions, and Data Stores) to access external information and perform actions.

The paper details agent architecture, including the model, tools, and orchestration layer, and explores various reasoning frameworks like ReAct and Chain-of-Thought.

It also discusses methods for enhancing model performance through targeted learning and provides examples using LangChain and Vertex AI. Finally, it summarizes the key components and future directions of agent development.

Related Articles

Google: Agents Companion

The document "Agents Companion" outlines advancements in generative AI agents, detailing an architecture that goes beyond traditional language models by integrating models, tools, and orchestration. It emphasizes the importance of Agent Ops—combining DevOps and MLOps principles—with rigorous automated and human-in-the-loop evaluation metrics and showcases the benefits of multi-agent systems for handling complex tasks.

Jeremy WeaverApril 4, 2025

Shadow AI Is Already Inside Every Government Agency

Unsanctioned AI use is already routine across federal agencies, and in government the exposure is statutory rather than commercial — Privacy Act records sent to commercial providers, federal records generated in systems the agency cannot subpoena, supply-chain restrictions under EO 13873, and mosaic classification spillage. This post maps each exposure to its legal basis and gives the data-classification tiers that decide which workloads need managed cloud, agency-controlled infrastructure, or a fully air-gapped deployment.

ibl.ai EngineeringAugust 4, 2026

The Open-Weight Tipping Point: Two 2-Trillion-Parameter Models

Two models above 2 trillion parameters became available as open weights in a single week: Moonshot's Kimi K3 at 2.8T with a 1M-token context, and Alibaba's Qwen 3.8-Max at 2.4T with 95B active per token. This post does the memory arithmetic on what it actually takes to serve models that size, prices the alternatives, and explains why the durable advantage is model-agnostic infrastructure rather than any single model.

ibl.ai EngineeringAugust 3, 2026

AI Agent Security Is an Infrastructure Problem, Not a Feature

Uber's security lead says securing AI agents is what keeps him up at night, and Google just shipped agent evaluation tooling to production. The tooling layer is maturing; the infrastructure question underneath it is not. This post explains why you cannot fully secure an agent whose reasoning runs on someone else's servers, and gives the five-question perimeter test to run on any agent platform before you sign.

ibl.ai EngineeringAugust 2, 2026

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