Multi-Agent Systems for Reinforcement Learning

Stumbled upon an LLM-based Trading System that Genuinely Impressed me. It feels less like a script and more like a Real-Investment Team. This Multi-Agent Architecture masterfully blends AI reasoning, market signals, and built-in risk governance. It does not just automate trades. It models how humans make decisions under pressure. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: 𝟏. 𝐌𝐮𝐥𝐭𝐢-𝐬𝐨𝐮𝐫𝐜𝐞 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 It pulls from everywhere: markets, news, earnings, Twitter, fundamentals. This is not just technical analysis it’s sentiment, narratives, and macro signals. 𝟐. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 𝐚𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫𝐦 𝐨𝐩𝐢𝐧𝐢𝐨𝐧𝐬 Each agent reads a different feed, builds a thesis, and takes a stance bullish or bearish. Then they debate. This isn’t a static pipeline it is a live reasoning loop. 𝟑. 𝐓𝐫𝐚𝐝𝐞𝐫 𝐚𝐠𝐞𝐧𝐭𝐬 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐭𝐡𝐞 𝐝𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧 They take that input and propose actual trades with justification. You don’t just get the “𝐰𝐡𝐚𝐭.” You get the “𝐰𝐡𝐲.” 𝟒. 𝐑𝐢𝐬𝐤 𝐦𝐨𝐝𝐞𝐥𝐬 𝐬𝐡𝐚𝐩𝐞 𝐭𝐡𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 A separate agent (or team) evaluates trades against risk profiles aggressive, neutral, conservative. This is where governance enters the loop. 𝟓. 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 𝐚𝐠𝐞𝐧𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐞 𝐟𝐢𝐧𝐚𝐥 𝐜𝐚𝐥𝐥 It weighs conviction, risk alignment, and timing and either executes or walks away. What is powerful here is not just the use of agents. It is how modular and human-like the process feels. - Research - Discussion - Proposal - Risk review - Execution Each part is Transparent. Tunable. Swappable. The future of AI is not just about faster automation. It is about designing systems that think and reason in steps just like we do. Here is the open repo if you want to dive deeper: https://lnkd.in/e2-RBEaK Where else do you think this kind of agentic architecture could apply? Let’s explore.

👁 Soon enterprise solution architects will be able to design complex System-Knowledge-Human-AI system for any existing enterprise use cases. In the below fraud detection & prevention use case in financial services, we designed four AI Agents interacting with human, systems, and knowledge : 1️⃣ AI Agent #1: Pattern Recognition Agent Role: Accelerate fraudulent activity identification for analysts. Knowledge & Memory: Fraud patterns and user behavior knowledge. Integrated Systems: Interfaces with transaction monitoring systems. Specificities: Specializes in real-time pattern and anomaly detection. 2️⃣ AI Agent #2: Investigation Assistant Agent Role: Supports analysts in verifying flagged transactions. Knowledge & Memory: Transaction history and known fraud methods. Integrated Systems: Taps into core banking and digital platforms. Specificities: Extracts data, provides context, and assesses risk. 3️⃣ AI Agent #3: Resolution Suggestion Agent Role: Proposes solutions for confirmed fraud cases. Knowledge & Memory: Past resolutions and related outcomes. Integrated Systems: Connects to incident management and customer platforms. Specificities: Analyzes scenarios, assesses impacts, and recommends actions. 4️⃣ AI Agent #4: Fraud Prevention Education Agent Role: Educates on fraud prevention. Knowledge & Memory: Fraud tactics and effective prevention methods. Integrated Systems: Works with customer channels, internal learning systems, or knowledge management systems. Specificities: Curates personalized content, updates dynamically, and nudges behavior. #llm #generativeai #multiagents

You’ve heard of AI agents. But what happens when they collaborate? That’s where multi-agent architecture comes in—and it’s powerful. Here’s the idea: You’re not building one big monolithic AI. You’re designing a team of specialized agents—each doing their own thing, but working together through planning, feedback, memory, and coordination. It’s kind of like a startup team: • One agent handles planning • Another reflects and optimizes performance • Another uses tools to execute • Another pulls the right data • And the whole system learns, evolves, and delivers results—constantly This PDF lays it out simply: input → orchestration → agent layers → storage → coordination → output → service. And it includes important pieces people often ignore like: • Real-time feedback loops • Vector stores + knowledge graphs • Governance, safety, and compliance built-in If you’re building in AI, or just trying to understand how modern AI systems can scale, this is worth a look. Skim it. Save it. You’ll probably come back to it later like I did.

🤖 New Guide: AI Agent Orchestration Patterns on the Azure Architecture Center Single AI agents often hit their limits with complex tasks. The future belongs to multi-agent orchestrations that break problems into specialized, collaborative units, that work together in ways that mimic human collaboration techniques. Our new comprehensive guide on Microsoft Learn covers five fundamental orchestration patterns: 🔗 Sequential: Chain agents for multi-stage processes ⚡ Concurrent: Multiple specialists tackle the same problem simultaneously 💬 Group chat: Agents collaborate through structured conversations for decision-making 🤝 Handoff: Intelligent routing where agents delegate to the most appropriate specialist 🎯 Magentic: For open-ended problems where the solution path needs to be discovered Perfect for architects moving beyond monolithic agent architectures. This article is brought to Microsoft Learn by the Azure Patterns & Practices team, with the help of some awesome subject matter experts. Shoutout to: Clayton Siemens, Davide Antelmo, Eric Zhu, Hema Alaganandam, James Lee, Mahdi Setayesh, Mark Taylor, Ritesh Modi, Samantha Brown, Shawn Henry, Tao Chen, and Yaniv Vaknin Read the full guide: https://lnkd.in/gEz8pMMd cc: Hans Yang, Luke Nyswonger, Martin Ekuan #AI #MachineLearning #Azure #SoftwareArchitecture #AgentOrchestration #MicrosoftLearn #SemanticKernel #AzureAIFoundry