Multi-Agent AI Workflow Observability Framework

I found the missing piece for building AI agent teams that actually collaborate! Common Ground is an open-source framework for creating teams of AI agents that tackle complex research and analysis tasks through true collaboration. Think of it as simulating a real consulting team: a Partner agent handles user interaction, a Principal agent breaks down complex problems, and specialized Associate agents execute the work. Key Features: • Advanced multi-agent architecture with Partner-Principal-Associate roles • Full observability with real-time Flow, Kanban, and Timeline views • Model agnostic with built-in Gemini integration via LiteLLM • Extensible tooling through Model Context Protocol (MCP) • Built-in project management and auto-updating RAG system The breakthrough? It transforms you from a passive prompter into an active "pilot in the cockpit" with deep visibility into not just what agents are doing, but why they're doing it. Perfect for building agents that handle multi-step workflows and strategic collaboration beyond simple command-response chains. It's 100% open-source. Link to the repo in the comments! ___ Connect with me → Shubham Saboo I share daily AI tips and opensource tutorials on AI Agents, RAG and MCP.

CSIRO researchers just unveiled AgentOps, a groundbreaking DevOps paradigm that solves the black box problem in LLM agents by enabling comprehensive observability across their entire lifecycle. While LLM agents show immense potential for automating complex tasks, their autonomous and non-deterministic behavior raises significant AI safety concerns. AgentOps addresses this by introducing a systematic way to trace and monitor agent behavior. Paper highlights: (1) Artifact relationship model - maps out the complex interactions between different components of an agent system, from reasoning and planning to execution and evaluation (2) Comprehensive taxonomy - provides a template for developers to implement proper monitoring and logging of agent activities (3) Systematic tracing approach - enables agent developers to monitor agent behavior, track artifacts, detect anomalies, and assign accountability This study is particularly relevant as most existing DevOps tools only focus on LLM-specific metrics and prompt management, leaving a critical gap in agent-specific observability. Paper https://lnkd.in/gnqCMWJ3 More posts on AI Agents https://lnkd.in/gpeDupnj

OpenAI just dropped Agents SDK for developers to create 'digital employees'. The most interesting part to me is introduction of new AI 'core primitives'. Translation: Any developer can now create AI agents that can understand requests and independently perform tasks like searching the web, going through your files, or even using your computer. First up, what is in OpenAI's Agent SDK (Software Development Kit) box? 1. Responses API: Combines simplicity with powerful tool-use capabilities. 2. Built-in tools: Web search, file search, and computer use functionalities. 3. Agents SDK: An open-source toolkit for orchestrating single and multi-agent workflows. 4. Integrated observability tools: For tracing and inspecting agent workflow execution. Here's the most exciting part, new AI core primitives and here is how OpenAI is defining these: 1. Agent: An LLM configured with instructions, tools, handoffs, and guardrails to execute tasks. 2. Tool: Functions the agent can call for external help, such as APIs, calculations, or file access. 3. Context: A mutable object storing state or shared resources passed between agents. 4. Output Types: Allows specifying structured final outputs instead of free-form text. 5. Handoffs: Mechanism for delegating or switching the conversation to a different agent. 6. Streaming: Emits partial/delta output events as the agent thinks or calls tools, useful for real-time UIs. 7. Tracing: Automatically captures a detailed trace of each "agentic run" for debugging, analytics, or record-keeping. 8. Guardrails: Validates inputs or outputs, checks policy, or halts execution if something is off-limits. Key Features in Action a. Handoffs: Enable multi-agent collaboration, allowing a parent agent to delegate tasks to specialized sub-agents based on language, expertise, or task complexity. b. Streaming: Delivers incremental updates, ideal for responsive user experiences. c. Tracing: Provides full visibility into agent workflows, critical for auditing, performance tuning, and compliance. d. Guardrails: Ensure input validation, output validation, and policy adherence. I'm excited because this means OpenAI's competitors will also be creating similar SDKs for developers to build upon. We are witnessing the race to create truly useful and reliable AI agents is heating up. What are your thoughts on OpenAI's announcement and the AI agent revolution? 🤖💼 #AIAgents #OpenAI #TechInnovation #FutureOfWork