The latest articles on DEV Community by Aditya Anarase (@aditya_anarase_c62d2dd4dc). https://dev.to/aditya_anarase_c62d2dd4dc https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3936264%2F6da6b681-1679-4d9c-9235-5d9d2911c487.jpg https://dev.to/aditya_anarase_c62d2dd4dc en Aditya Anarase Sun, 17 May 2026 12:59:11 +0000 https://dev.to/aditya_anarase_c62d2dd4dc/captain-cool-ai-building-a-multi-agent-ipl-strategy-engine-with-google-gemini-25-59a4 https://dev.to/aditya_anarase_c62d2dd4dc/captain-cool-ai-building-a-multi-agent-ipl-strategy-engine-with-google-gemini-25-59a4 <h1> Captain Cool AI — Building a Multi-Agent IPL Strategy Engine with Google Gemini 2.5 </h1> <p>Cricket is a captain’s game.</p> <p>In the IPL, tactical decisions change matches ball-by-ball:</p> <ul> <li>Who bowls the next over?</li> <li>Should spin continue under dew?</li> <li>When should the Impact Player be used?</li> <li>Should the captain attack or defend the boundary?</li> </ul> <p>We wanted to build an AI system that thinks like a real IPL captain.</p> <p>So we built <strong>Captain Cool AI</strong> — a multi-agent IPL tactical command center powered entirely by the Google Gemini ecosystem.</p> <p>Instead of creating a generic cricket chatbot, we designed a system where multiple AI agents debate strategy like a real dugout before committing to a final tactical call.</p> <h1> The Idea </h1> <p>The goal was simple:</p> <blockquote> <p>Simulate how elite IPL captains think under pressure.</p> </blockquote> <p>Captain Cool AI allows users to input:</p> <ul> <li>current score</li> <li>overs</li> <li>wickets</li> <li>target</li> <li>pitch conditions</li> <li>dew factor</li> <li>bowling options</li> <li>batting pair</li> </ul> <p>The system then:</p> <ol> <li>Runs a multi-agent tactical debate</li> <li>Challenges decisions internally</li> <li>Calculates probabilities</li> <li>Explains the final strategy in authentic cricket language</li> </ol> <p>The result feels less like an AI assistant and more like an IPL strategy war room.</p> <h1> Tech Stack </h1> <p>We built the entire system on Google’s AI ecosystem.</p> <h2> Core Stack </h2> <ul> <li>Next.js</li> <li>TailwindCSS</li> <li>FastAPI</li> <li>Google Gemini 2.5 Flash</li> <li>Google Gemini 2.5 Pro</li> <li>Google ADK</li> <li>Gemini Function Calling</li> </ul> <h1> Architecture Overview </h1> <p>Captain Cool AI uses a multi-agent architecture where every agent has:</p> <ul> <li>its own role</li> <li>its own system prompt</li> <li>independent Gemini calls</li> <li>tactical specialization</li> <li>shared contextual memory</li> </ul> <h1> The 4 AI Agents </h1> <h2> 1. Match Analyst Agent </h2> <p>The Match Analyst acts like a Moneyball-style cricket strategist.</p> <p>Responsibilities:</p> <ul> <li>analyze batter vs bowler matchups</li> <li>calculate pressure index</li> <li>evaluate pitch behavior</li> <li>estimate win probability</li> <li>identify tactical trends</li> </ul> <p>Example:</p> <blockquote> <p>“Hardik Pandya strikes at 192 against pace-on deliveries in death overs but drops significantly against wide yorkers.”</p> </blockquote> <h2> 2. Tactical Captain Agent </h2> <p>This agent behaves like a calm IPL captain under pressure.</p> <p>Responsibilities:</p> <ul> <li>decide bowling changes</li> <li>recommend field placements</li> <li>suggest Impact Player timing</li> <li>manage over-by-over tactics</li> </ul> <p>Example:</p> <blockquote> <p>“Bring Pathirana now and force Hardik to access the off-side boundary.”</p> </blockquote> <h2> 3. Devil’s Advocate Agent </h2> <p>This was the most important part of the system.</p> <p>Instead of blindly agreeing, this agent aggressively challenges tactical decisions.</p> <p>Responsibilities:</p> <ul> <li>expose tactical risks</li> <li>question emotional decisions</li> <li>propose alternative strategies</li> <li>force revisions</li> </ul> <p>Example:</p> <blockquote> <p>“Saving Rashid for over 19 is dangerous because dew will reduce grip significantly.”</p> </blockquote> <p>This creates genuine agentic behavior instead of fake multi-role prompting.</p> <h2> 4. Commentary Expert Agent </h2> <p>The final agent explains the tactical decision like a live cricket commentator.</p> <p>Responsibilities:</p> <ul> <li>convert tactical reasoning into fan-friendly commentary</li> <li>explain why-this-not-that</li> <li>summarize risk/reward tradeoffs</li> </ul> <p>Example:</p> <blockquote> <p>“This is classic death-over manipulation. The captain is forcing the batter to hit against the angle under pressure.”</p> </blockquote> <h1> Multi-Agent Tactical Debate </h1> <p>The core innovation was the debate loop.</p> <p>Instead of using a single Gemini prompt pretending to be multiple agents, we implemented a real reasoning workflow:</p> <ol> <li>Match Analyst evaluates conditions</li> <li>Tactical Captain proposes a strategy</li> <li>Devil’s Advocate critiques the plan</li> <li>Tactical Captain revises or defends</li> <li>Commentary Expert explains the final decision</li> </ol> <p>This created tactical discussions that felt surprisingly authentic.</p> <h1> Shared Match Memory </h1> <p>We implemented shared contextual memory across all agents.</p> <p>The system tracks:</p> <ul> <li>previous overs</li> <li>batter momentum</li> <li>bowler rhythm</li> <li>yorker success rate</li> <li>pressure moments</li> <li>tactical patterns</li> </ul> <p>This allows agents to reference earlier events naturally.</p> <p>Example:</p> <blockquote> <p>“Siraj missed yorkers in the previous over, so the captain should avoid repeating the same plan.”</p> </blockquote> <h1> Gemini Tool Calling </h1> <p>To make the system truly agentic, we integrated Gemini function calling with tactical tools.</p> <h2> Tools Used </h2> <h3> Win Probability Engine </h3> <p>Calculates:</p> <ul> <li>win probability</li> <li>projected score</li> <li>pressure index</li> <li>momentum shifts</li> </ul> <h3> Matchup Analyzer </h3> <p>Evaluates:</p> <ul> <li>batter vs bowler records</li> <li>spin vs pace effectiveness</li> <li>death-over strike rates</li> </ul> <h3> Pitch &amp; Dew Analyzer </h3> <p>Analyzes:</p> <ul> <li>dew conditions</li> <li>pitch slowdown</li> <li>venue behavior</li> </ul> <p>These tools allowed agents to reason using real tactical data instead of static prompting.</p> <h1> Live Match Scraper </h1> <p>One of the most exciting features was the live match scraper.</p> <p>Users can paste:</p> <ul> <li>Cricbuzz URLs</li> <li>ESPN Cricinfo URLs</li> </ul> <p>The system then:</p> <ol> <li>fetches live match content</li> <li>extracts score/state using Gemini</li> <li>auto-populates the tactical dashboard</li> <li>starts tactical analysis instantly</li> </ol> <p>This made the experience feel like a real AI-powered IPL command center.</p> <h1> Tactical Realism </h1> <p>We wanted every output to feel like real cricket intelligence.</p> <p>So the system generates:</p> <ul> <li>bowling changes</li> <li>field placements</li> <li>tactical intent</li> <li>counterfactual reasoning</li> <li>alternative rejected strategies</li> </ul> <p>Example:</p> <blockquote> <p>“Bowling Rashid now improves win probability from 61% to 68% because dew may reduce spin grip later.”</p> </blockquote> <p>Field setups are also generated dynamically:</p> <ul> <li>deep square leg</li> <li>long-off back</li> <li>third-man fine</li> <li>cover inside the ring</li> </ul> <p>This made the strategies feel much closer to actual IPL captaincy.</p> <h1> Captaincy Personality Modes </h1> <p>We added different captain personalities to simulate tactical styles.</p> <p>Modes:</p> <ul> <li>Dhoni Mode</li> <li>Rohit Mode</li> <li>Gambhir Mode</li> <li>Cummins Mode</li> </ul> <p>Each mode changes:</p> <ul> <li>aggression level</li> <li>bowling strategy</li> <li>field placement logic</li> <li>tactical tone</li> <li>risk appetite</li> </ul> <p>This added personality and realism to the system.</p> <h1> Frontend Experience </h1> <p>The UI was designed to feel like:</p> <blockquote> <p>an IPL tactical command center</p> </blockquote> <p>instead of:</p> <blockquote> <p>a generic AI chatbot.</p> </blockquote> <p>We built:</p> <ul> <li>live tactical debate timeline</li> <li>streaming agent responses</li> <li>confidence meters</li> <li>win probability cards</li> <li>premium dark sports analytics UI</li> <li>command-center aesthetics</li> </ul> <p>The goal was immersion and realism.</p> <h1> Example Tactical Scenario </h1> <p>Scenario:</p> <ul> <li>MI need 24 off 10</li> <li>Hardik Pandya on strike</li> <li>Heavy dew</li> <li>Pathirana has one over remaining</li> </ul> <h2> Tactical Captain </h2> <blockquote> <p>“Bring Pathirana now with wide yorkers.”</p> </blockquote> <h2> Devil’s Advocate </h2> <blockquote> <p>“Hardik targets pace-on deliveries. Jadeja may force mistimed lofts.”</p> </blockquote> <h2> Final Decision </h2> <blockquote> <p>“Use Pathirana with deep third and long-off protection.”</p> </blockquote> <h2> Commentary Expert </h2> <blockquote> <p>“This is classic death-over manipulation under pressure.”</p> </blockquote> <h1> Challenges </h1> <p>Some of the biggest engineering challenges included:</p> <ul> <li>creating believable tactical debate</li> <li>maintaining shared memory</li> <li>coordinating multiple Gemini agents</li> <li>balancing realism with speed</li> <li>designing cricket-authentic explanations</li> <li>building smooth orchestration workflows</li> </ul> <h1> Final Thoughts </h1> <p>Captain Cool AI was built around one question:</p> <blockquote> <p>“What would a great IPL captain do next?”</p> </blockquote> <p>By combining:</p> <ul> <li>Google Gemini 2.5</li> <li>multi-agent orchestration</li> <li>tactical explainability</li> <li>live match parsing</li> <li>shared contextual memory</li> </ul> <p>we created an AI-powered IPL strategy engine capable of debating cricket tactics like a real IPL dugout.</p> <p>This project showed how agentic AI systems can move far beyond chatbots into collaborative tactical intelligence systems.</p> <h1> GitHub Repository </h1> <p><a href="https://github.com/Aditya-Anarse/captain-cool-aii.git" rel="noopener noreferrer">https://github.com/Aditya-Anarse/captain-cool-aii.git</a></p> <h1> Built With </h1> <ul> <li>Google Gemini 2.5</li> <li>Google ADK</li> <li>Next.js</li> <li>FastAPI</li> <li>TailwindCSS</li> <li>Gemini Function Calling</li> </ul> ai gdgcloudpune