Tag
This article details how to structure multi-agent AI teams for investment research, using open-source projects like TradingAgents and the Bloome platform. It emphasizes that the key to effective agent collaboration is the organizational architecture, not the model intelligence.
Claude Code releases dynamic workflows, enabling Claude to create custom harnesses for tasks like research, security analysis, agent teams, and code review, all natively within the tool.
Harness is a Claude Code plugin that automatically generates a multi-agent team architecture based on a one-sentence description. It comes with 6 collaboration modes and 100 ready-made configurations, helping Claude Code transition from solo operation to team collaboration.
A Korean developer released Harness, a Claude Code plugin that automatically generates agent team definitions and skills based on a single prompt. It includes six team architecture patterns and reports impressive A/B test results (60% quality improvement, n=15).
Open Envelope provides an open JSON Schema for defining AI agent teams with roles, handoffs, human checkpoints, and access policies, aiming for portability across frameworks.
An Anthropic engineer demonstrated how a single person can orchestrate five AI agents to simultaneously code, test, review, and deploy software, and compiled a guide on building effective agent teams.
The article argues that multi-agent systems require a runtime infrastructure layer rather than better prompts, citing releases from MiniMax, OpenAI, Google, and Anthropic. It highlights the separation of worker and verifier roles and the overhead costs of multi-agent setups.
Announces the ability to run fully local agent teams using NousResearch Hermes agents on systems with 24-128GB unified memory. Each agent has its own Hermes session and works collaboratively via a local orchestrator on long-running tasks.