AI Solutions for Cross-Functional Project Teams

Not all AI agents are created equal — and the framework you choose shapes your system's intelligence, adaptability, and real-world value. As we transition from monolithic LLM apps to 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, developers and organizations are seeking frameworks that can support 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, and 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘁𝗮𝘀𝗸 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. I created this 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 to help you navigate the rapidly growing ecosystem. It outlines the 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀, 𝗮𝗻𝗱 𝗶𝗱𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 of the leading platforms — including LangChain, LangGraph, AutoGen, Semantic Kernel, CrewAI, and more. Here’s what stood out during my analysis: ↳ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 is emerging as the go-to for 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹, 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 — perfect for self-improving, traceable AI pipelines.  ↳ 𝗖𝗿𝗲𝘄𝗔𝗜 stands out for 𝘁𝗲𝗮𝗺-𝗯𝗮𝘀𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, useful in project management, healthcare, and creative strategy.  ↳ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 quietly brings 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 to the agent conversation — a key need for regulated industries.    ↳ 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 simplifies the build-out of 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀 through robust context handling and custom roles.  ↳ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 is refreshingly light — ideal for 𝗿𝗮𝗽𝗶𝗱 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗺𝗮𝗹𝗹-𝗳𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀.  ↳ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 continues to shine as a sandbox for 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 and open experimentation. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗵𝘆𝗽𝗲 — 𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹𝘀: - Are you building enterprise software with strict compliance needs?   - Do you need agents to collaborate like cross-functional teams?   - Are you optimizing for memory, modularity, or speed to market? This visual guide is built to help you and your team 𝗰𝗵𝗼𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗰𝗹𝗮𝗿𝗶𝘁𝘆. Curious what you're building — and which framework you're betting on?

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.

The most powerful use of AI at work won’t be solo. It will be shared. Ben Thompson recently wrote about a compelling use case: how he and his assistant collaborated with a single LLM chat. An example of a shared assistant for team coordination and synthesis. I’ve been thinking about this a lot too. At Dropbox, we’re building toward this future with Dash, our new AI workspace, and specifically with Stacks, a way for teams to organize, track, and reason across all the work happening in a project. Stacks are designed for collaborative intelligence. Teams can pull in docs, links, and tools from anywhere, ask questions about the work, and get AI-generated summaries that evolve as the project does. It’s a persistent shared memory that helps teams move faster, stay aligned, and reduce the drag of context loss. But coordination is just the first step. There are four basic configurations for how humans and LLMs might collaborate: 1. One person working with many agents. The classic orchestration model. Think of a PM using agents for research, writing, and planning. Most solo AI workflows live here today. 2. One agent working with many agents. A tool-using agent. This is the core of agentic infrastructure work. AutoGPT, Devin, and others. A lot of current technical energy is focused here. 3. Many people working with one LLM. A shared assistant for a team. Ben’s focus. This supports team-level memory, project synthesis, and aligned decisions. It’s emerging now. 4. Many people working with many agents, all coordinated through a shared LLM. This is the frontier. Imagine a team approves a campaign plan. Their shared LLM doesn’t just spin up agents. It engages the creative director, strategist, and producer, plus their teams (human and AI). The LLM knows the full context. It routes tasks, surfaces blockers, loops people in, and maintains alignment across the entire system. This isn’t a person using a tool. It’s people and AI, working together, across roles and workflows, with shared direction and shared memory. The shift is from individual productivity to shared intelligence. And the opportunity doesn’t stop at coordination. Negotiation. Conflict resolution. Team morale. Goal tracking. These are the complex, often messy parts of work where tools today tend to disappear. But this is exactly where AI can help. Not by replacing humans, but by holding context, clarifying intent, and accelerating momentum. That’s the future we’re building toward with Dash. AI that doesn’t just respond to prompts. It shows up in the group chat. It remembers the project goals. It knows what’s next. And it helps the whole team move. The future of work is multiplayer. And the most powerful teams will be human and AI, together, all the way down.