AI-Agents

Microsoft Foundry Agent Service Build 2026 is Microsoft’s production platform for running AI agents with managed hosting, memory, tool access, evaluations, and governance. The practical shift is that teams can keep their preferred agent framework while moving runtime, identity, observability, and policy controls into a managed Azure control plane. What Did Microsoft Announce for Foundry Agent Service at Build 2026? Microsoft Foundry Agent Service Build 2026 is a set of production agent capabilities around hosted runtimes, Toolboxes, managed Memory, Foundry IQ, evaluations, and governance controls. Microsoft positioned the service as the operating layer for enterprise agents, while Gartner predicts 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. The important developer news is not a single model endpoint. It is the packaging of agent execution, identity, lifecycle management, tool calling, long-term context, tracing, evaluation, and compliance into one managed service. Hosted agents let teams bring code from Microsoft Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK, or custom runtimes. Toolboxes and Memory move common platform concerns out of each application. The takeaway: Build 2026 made Foundry Agent Service look less like a demo builder and more like infrastructure for operating agents repeatedly. ...

Microsoft Work IQ APIs give developers a permission-aware way to build Microsoft 365 agents that reason over email, meetings, files, people, chats, and workflow context. For teams preparing for the June 16, 2026 GA release, the practical decision is how to use A2A, MCP, or REST without rebuilding Graph search and RAG plumbing from scratch. What Are Microsoft Work IQ APIs and What Changes on June 16, 2026? Microsoft Work IQ APIs are agent-oriented Microsoft 365 APIs that expose synthesized work context through A2A, MCP, and REST endpoints instead of forcing developers to manually join mail, calendar, chat, file, and people signals. Microsoft announced on June 2, 2026 that Work IQ APIs are scheduled for general availability on June 16, 2026, with GA endpoints for Agent-to-Agent, a redesigned remote MCP server, and REST API access. The important change is not just another endpoint family; it is a production contract for enterprise agents that need permission-trimmed business context, admin controls, and consumption billing. Existing Microsoft 365 governance remains part of the access model, while developers get a higher-level surface for agent workflows. The takeaway is that Work IQ should be evaluated as a context and action layer for Microsoft 365 agents, not as a simple Microsoft Graph replacement. ...

Windows Intelligent Terminal is Microsoft’s experimental AI-assisted fork of Windows Terminal for developers who want an agent to understand shell context, diagnose errors, and help manage command-line work. Treat the 0.1 release as a preview: useful for testing agentic workflows, not a replacement for your stable terminal. What Is Windows Intelligent Terminal? Windows Intelligent Terminal is an experimental fork of Windows Terminal that adds an AI agent layer directly into the command-line environment. Microsoft announced Intelligent Terminal 0.1 as a preview rather than a stable replacement, and Stack Overflow’s 2025 Developer Survey shows why the surface matters: 46.9% of professional developers still use Windows at work. The important shift is not prettier autocomplete. The terminal can expose shell-aware context to an agent, including recent commands, command output, failed builds, running processes, and workspace state. That gives the assistant a better starting point than a generic chatbot receiving pasted errors. Because it is a fork, existing Windows Terminal concepts still matter: profiles, tabs, panes, PowerShell, WSL, command palette actions, and settings remain the mental model. The takeaway: Windows Intelligent Terminal is best understood as a preview of an agentic terminal, not a finished enterprise terminal product. ...

The best multi-agent framework in 2026 depends on your main failure mode: choose LangGraph for explicit state and recovery, CrewAI for fast role-based workflows, Google ADK for GCP and Gemini-native systems, Strands Agents for AWS-oriented production agents, and Agno for runtime APIs, governance, and operational control. Which Multi-Agent Framework Should You Pick in 2026? A multi agent framework comparison 2026 should start with fit, not hype: LangGraph 1.2.4, CrewAI 1.14.7, Google ADK 2.2.0, Strands Agents 1.43.0, and Agno 2.6.13 solve different production problems. LangGraph is the best default when failures must resume from checkpoints and branches must be explicit. CrewAI is the fastest path when the work maps cleanly to roles such as researcher, analyst, reviewer, and writer. Google ADK is strongest when your platform decision is already GCP, Gemini, and Google enterprise deployment. Strands Agents fits teams building model-driven agents with AWS-style production expectations and OpenTelemetry traces. Agno fits teams that need AgentOS APIs, sessions, tracing, scheduling, RBAC, and audit logs around agents. The clear takeaway: pick the framework whose control model matches the way your system fails. ...

Amazon Bedrock AgentCore is AWS’s production platform for deploying, securing, observing, and governing AI agents built with frameworks such as LangGraph, CrewAI, LlamaIndex, and Strands Agents. Use it when your agent needs managed runtime isolation, enterprise identity, tool governance, memory, evaluation, and AWS-native operations instead of another prototype server. What Is Amazon Bedrock AgentCore? Amazon Bedrock AgentCore is a managed AWS platform for taking code-first AI agents from local development to production operations with runtime hosting, memory, identity, tool access, observability, policy, browser automation, and code execution. AWS made AgentCore generally available on October 13, 2025, and GA added VPC, AWS PrivateLink, AWS CloudFormation, and resource tagging across its services. The important detail is that AgentCore is not a new prompt format or a single agent framework. It is the production control plane around agents you already build with frameworks such as LangGraph, CrewAI, LlamaIndex, and Strands Agents, and it can work with different foundation models. The platform matters because production agents fail in places demos ignore: credentials, network boundaries, tool authorization, memory drift, tracing, replay, cost, and incident response. The takeaway: Amazon Bedrock AgentCore is the AWS operations layer for serious agent deployments. ...

The google adk a2a protocol pairing gives developers a practical way to build agents in Google ADK while exposing them through the open Agent2Agent protocol. Use ADK for agent logic, workflows, tools, and state; use A2A when those agents need to collaborate across frameworks, clouds, services, or organizational boundaries. What Do Google ADK and A2A Solve Together? Google ADK and A2A solve different parts of the same multi-agent system: ADK builds and runs the agent, while A2A lets that agent communicate with other agents through a shared protocol. Google announced ADK Python v1.0.0 as production-ready at Google I/O 2025, and ADK Python v2.2.0 was the latest release in the research brief dated June 12, 2026. A2A moved from a Google-led protocol into an open standard hosted by the Linux Foundation, with more than 150 supporting organizations announced on April 9, 2026. The practical result is a cleaner boundary: teams can use ADK for prompts, tools, graph workflows, memory, and orchestration, then publish selected capabilities through A2A Agent Cards, tasks, messages, and artifacts. The takeaway is simple: ADK is your implementation framework, and A2A is your interoperability contract. ...

If you want an AWS-native AI workflow fast, Strands is the practical middle ground: you get a lightweight agent framework, native MCP-style tool ergonomics, and an upgrade path to Bedrock AgentCore without rewriting core logic. In the first 20 minutes you can run a tool-calling agent that answers real customer questions, saves session context, and is deployable to Lambda. Why is this tutorial AWS-native (Strands vs alternatives)? Strands is an agent SDK that gives AWS-focused teams a small orchestration surface and practical escape hatches, while keeping the execution model familiar enough to adopt quickly. The strands agents quickstart aims for a first working agent in under 20 minutes, and AWS’s own serverless guidance says enterprise adoption of agentic capabilities could rise to 33% by 2028 from under 1% today. In July 2026 GitHub statistics still show the ecosystem split: Strands SDK has around 6,106 stars, far smaller than LangGraph’s 34,458 and OpenAI Agents Python’s 27,084, which means it is lighter and less opinionated but not yet overengineered. For teams already shipping on AWS, Strands’ advantage is reduced infrastructure churn: you can start with plain Lambda functions and later move to Bedrock AgentCore when runtime controls and session management become a governance requirement. For this reason, Strands is usually the right first move when speed and AWS-native operations matter. ...

Google ADK gives you a production-oriented path for first-pass AI agents because it packages model orchestration, tool calls, sessions, and runtime execution together instead of treating them as separate integrations. In 2026, you can run a first agent in under 20 minutes with the built-in quickstart flows, then keep the same foundation while you scale to multi-agent and enterprise observability features like OpenTelemetry, self-healing plugins, and session persistence. I built several internal prototypes with ADK in the last quarter, and the biggest difference is how quickly you can move from “single prompt” to “task graph” without replacing your entire stack. This tutorial is the one I wish existed: no fluff, just the version-specific setup choices, concrete files, and production traps that matter. ...

Microsoft Agent Framework 1.0 is the best 2026 choice for Azure, .NET, Semantic Kernel, and AutoGen migration teams; LangGraph is the strongest independent runtime for durable Python state graphs; AutoGen is now mainly for existing research prototypes and legacy multi-agent experiments. Which framework should developers pick in 2026? Microsoft Agent Framework 1.0 vs AutoGen LangGraph is not a three-way tie in 2026: Microsoft announced Agent Framework 1.0 on April 3, 2026 as a production-ready .NET and Python SDK, LangGraph 1.0 became generally available on October 22, 2025, and AutoGen now points new users toward Microsoft Agent Framework. Pick Microsoft Agent Framework when your production path depends on Azure AI Foundry, .NET services, Semantic Kernel inheritance, MCP, A2A, or enterprise governance. Pick LangGraph when you need explicit state graphs, durable execution, streaming, human approval, persistence, and a framework that stays cloud-neutral. Keep AutoGen only when an existing prototype depends heavily on AgentChat, GroupChat, or research-style agent conversations and the migration cost is not justified yet. The practical takeaway: choose the framework that matches your operational surface, not the one with the most impressive demo. ...

Durable execution is what moves AI agents from demo logic to production service: the ability to resume correctly after failure, avoid duplicate actions, and preserve conversational and task state. In teams where this is missing, incidents look random because retries, restarts, and tool calls desynchronize. In production, the first rule is to design for interruption so that every workflow can fail and still complete business goals safely. Why is durable execution table stakes for AI-agent production in 2026? Durable execution is the reliability contract that keeps an AI workflow correct after crashes, rollouts, and transient infra failures by preserving state and controlling replay behavior. In 2026, Stack Overflow’s developer pulse sample reported daily AI-agent usage at work growing from 14% in 2025 to 37%, showing adoption outpacing execution maturity. In practical terms, durability becomes critical because most failures occur in orchestration, not model inference. During an internal triage rollout, a single worker restart caused 12% of jobs to re-run and duplicate CRM updates because checkpoint recovery was missing around tool outputs; that one issue created several hours of cleanup, delayed SLAs, and support churn. Takeaway: in AI ops, durability is the operational baseline, and every missing checkpoint is an incident waiting to happen. ...