Tag: Multi-Agent
Posts with this tag
- AI Agent Guide: Architecture, Tools, Evaluation, and Enterprise Delivery
A practical guide to agents versus workflows, single- and multi-agent architecture, tools and MCP, state and memory, evaluation, security, and the path from PoC to production.
- From Multi-Agent Architecture to Recruiting an AI Employee in Two Minutes: AWS × Super 8 (ORRA) Enterprise Implementation
A summary of the AWS and Super 8 (ORRA) session: the single agent decision loop, three major multi-agent orchestration patterns (Graph/Swarm/Workflow), A2A communication, Amazon Bedrock AgentCore core components, and how ORRA allows business users to build and deploy AI employees in two minutes using Job Descriptions.
- Google ADK 2.0: Workflow Graphs, Task Collaboration, and HITL Boundaries
A source-grounded analysis of how ADK 2.0 separates deterministic routing from LLM reasoning, with production boundaries for workflows, tasks, human approval, and durable state.
- Anthropic Introduces Claude Tag: Making Claude a Permanent AI Teammate for Your Team
Anthropic has released Claude Tag, designed specifically for team collaboration. By tagging @Claude in Slack, AI becomes a virtual teammate that proactively participates in discussions, executes asynchronous tasks, and continuously learns. This article details its core features, usage, target audience, and billing model.
- Google Releases Agentic Resource Discovery Specification: The 'Yellow Pages of Capabilities' for the AI Agent Era
An in-depth analysis of the open specification Agentic Resource Discovery (ARD) released by Google in June 2026. This specification aims to standardize how AI Agents discover, verify, and connect with tools, skills, and other Agents in distributed systems, solving the core pain point of multi-agent collaboration: 'How do I find a trusted partner?'
- LangChain Dissects Agent Harness: From Model Capabilities to Deliverable Work Engines
A deep dive into LangChain's long-form article: The formal definition of Harness, the component chain deduced from expected behaviors (Files, Bash, Sandbox, Memory, Context Rot, Ralph Loop), and the insights from model-harness co-training and Terminal Bench.
- 16 Parallel Claudes Building a C Compiler: Anthropic's Agent Teams and Long-Running Harness Experiments
A deep dive into Nicholas Carlini's experiment: nearly 2,000 sessions, about $20,000 in API costs, and a 100,000-line Rust compiler capable of compiling Linux 6.9—exploring task locking, test harnesses, GCC oracle, multi-role specialization, and capability boundaries.
- Building Effective AI Agents: An Overview of Architecture Patterns and Implementation Strategies
Adapted from Anthropic's 'Building Effective AI Agents': From single agents to multi-agent collaboration, common architectural patterns, workflow design, and how to choose the right architecture based on control requirements, problem complexity, and resources.
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