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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?

Choosing the right LLM for your AI agent isn't about selecting the most powerful model. It's about finding the right capabilities for your specific use case and limitations. Different tasks require different strengths, whether it's reasoning through complex documents, conducting real-time research, or working efficiently on mobile devices. Understanding these eight key AI agent patterns helps you choose models that perform best for your actual needs instead of just impressive benchmarks. Here's how to match LLMs to your specific AI agent needs: 🔹 Web Browsing & Research Agents: You need models that are good at gathering information and market insights in real-time. GPT-4o with browsing capabilities, Perplexity API, and Gemini 1.5 Pro with API access work well because they can quickly process live web data and gather findings from various sources. 🔹 Document Analysis & RAG Systems: For contract analysis, legal research, and customer support bots, look for models that excel at understanding the context from retrieved documents. GPT-4o, Claude 3 Sonnet, Llama 3 fine-tuned versions, and Mistral with RAG pipelines handle long documents effectively. 🔹 Coding & Development Assistants: Automatic code generation and debugging need models trained specifically for programming tasks. GPT-4o, Claude 3 Opus, StarCoder2, and CodeLlama 70B understand code structure, troubleshoot issues, and explain complex programming concepts better than general models. 🔹 Specialized Domain Applications: Medical assistants, legal co-pilots, and enterprise Q&A bots benefit from specialized fine-tuning. Llama 3, Mistral fine-tuned versions, and Gemma 2B are most effective when customized for specific industries, regulations, and technical terms. Match your model choice to your deployment constraints. Cloud-based agents can use powerful models like GPT-4o and Claude, while edge devices need efficient options like Mistral 7B or TinyLlama. Start with general-purpose models for prototyping. Then optimize with specialized or fine-tuned versions once you know your specific performance needs. #llm #aiagents

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

A few months ago, a colleague screamed at Microsoft Copilot like he was auditioning for Bring Me The Horizon. He typed, “Make this into a presentation.” Copilot spat out something. He yelled, “NO, I SAID PROFESSIONAL!” It revised it. Still wrong. “WHY ARE YOU SO STUPID?” And that, dear reader, is when it hit me. It’s not the AI. It’s you. Or rather, your prompts. So, if you've ever felt like ChatGPT, Copilot, Gemini, or any of those AI Agents are more "artificial" than "intelligent"? Then rethink how you’re talking to them. Here are 10 prompt engineering fundamentals that’ll stop you from sounding like you're yelling into the void. 1. Lead with Intent. Start with a clear command: “You are an expert…,” “Generate a monthly report…,” “Translate this to French…" This orients the model instantly. 2. Scope & Constraints First. Define boundaries up front. Length limits, style guides, data sources, even forbidden terms. 3. Format Your Output. Specify JSON schema, markdown headers, or table columns. Models love explicit structure over free form prose. 4. Provide Minimal, High Quality Examples. Two or three exemplar Q→A pairs beat a paragraph of explanation every time. 5. Isolate Subtasks. Break complex workflows into discrete prompts (chain of thought). One prompt per action: analyze, summarize, critique, then assemble. 6. Anchor with Delimiters. Use triple backticks or XML tags to fence inputs. Cuts hallucinations in half. 7. Inject Domain Signals. Name specific frameworks (“Use SWOT analysis,” “Apply the Eisenhower Matrix,” “Leverage Porter’s Five Forces”) to nudge depth. 8. Iterate Rapidly. Version your prompts like code. A/B test variations, track which phrasing yields the cleanest output. 9. Tune the “Why.” Always ask for reasoning steps. Always. 10. Template & Automate. Build parameterized prompt templates in your repo. Still with me? Good. Bonus tips. 1. Token Economy Awareness. Place critical context in the first 200 tokens. Anything beyond 1,500 risks context drift. 2. Temperature vs. Prompt Depth. Higher temperature amplifies creativity. Only if your prompt is concise. Otherwise you get noise. 3. Use “Chain of Questions.” Instead of one long prompt, fire sequential, linked questions. You’ll maintain context and sharpen focus. 4. Mirror the LLM’s Own Language. Scan model outputs for phrasing patterns and reflect those idioms back in your prompts. 5. Treat Prompts as Living Docs. Embed metrics in comments: note output quality, error rates, hallucination frequency. Keep iterating until ROI justifies the effort. And finally, the bit no one wants to hear. You get better at using AI by using AI. Practice like you’re training a dragon. Eventually, it listens. And when it does, it’s magic. You now know more about prompt engineering than 98% of LinkedIn. Which means you should probably repost this. Just saying. ♻️

If you're using AI agents just to speed things up, you're missing their real value. Working with agents isn’t about shortcuts. It’s about designing collaborative systems that think with you. And this is how it should work: → Start with context Before you ask for outputs, define your goals, your audience, and the “why” behind your initiative. Agents perform best when they understand the bigger picture. → Design the workflow together Map out how agents and humans will interact. Who leads what? What tools are involved? What feedback loops do you need? → Only then, begin prompting This is where most teams start. But if you haven’t aligned on strategy, you’ll get fragmented results. At Mchange, we learned this the hands-on way. We had no background in marketing or content creation. But our AI agent team helped us build a content workflow from the ground up. It looks like this: → We set the mission: who we want to reach and why → We share that with our agents, often including docs, data, and vision → Together, we design the content flow and assign agent roles →Only then do we prompt for drafts, visuals, and distribution plans And the best part, The more we share up front, the more strategic and creative our outputs become. AI doesn’t just support our process, it teaches us how to improve it. Because when agents understand why something matters, they help you figure out how to make it matter more. That’s the real shift. AI inot as a tool, but as a thinking partner in your system. If you want deeper insights into how agent–human collaboration should look like DM me or book a call on our website. And remember, create value, not hype.

Teams will increasingly include both humans and AI agents. We need to learn how best to configure them. A new Stanford University paper "ChatCollab: Exploring Collaboration Between Humans and AI Agents in Software Teams" reveals a range of useful insights. A few highlights: 💡 Human-AI Role Differentiation Fosters Collaboration. Assigning distinct roles to AI agents and humans in teams, such as CEO, Product Manager, and Developer, mirrors traditional team dynamics. This structure helps define responsibilities, ensures alignment with workflows, and allows humans to seamlessly integrate by adopting any role. This fosters a peer-like collaboration environment where humans can both guide and learn from AI agents. 🎯 Prompts Shape Team Interaction Styles. The configuration of AI agent prompts significantly influences collaboration dynamics. For example, emphasizing "asking for opinions" in prompts increased such interactions by 600%. This demonstrates that thoughtfully designed role-specific and behavioral prompts can fine-tune team dynamics, enabling targeted improvements in communication and decision-making efficiency. 🔄 Iterative Feedback Mechanisms Improve Team Performance. Human team members in roles such as clients or supervisors can provide real-time feedback to AI agents. This iterative process ensures agents refine their output, ask pertinent questions, and follow expected workflows. Such interaction not only improves project outcomes but also builds trust and adaptability in mixed teams. 🌟 Autonomy Balances Initiative and Dependence. ChatCollab’s AI agents exhibit autonomy by independently deciding when to act or wait based on their roles. For example, developers wait for PRDs before coding, avoiding redundant work. Ensuring that agents understand role-specific dependencies and workflows optimizes productivity while maintaining alignment with human expectations. 📊 Tailored Role Assignments Enhance Human Learning. Humans in teams can act as coaches, mentors, or peers to AI agents. This dynamic enables human participants to refine leadership and communication skills, while AI agents serve as practice partners or mentees. Configuring teams to simulate these dynamics provides dual benefits: skill development for humans and improved agent outputs through feedback. 🔍 Measurable Dynamics Enable Continuous Improvement. Collaboration analysis using frameworks like Bales’ Interaction Process reveals actionable patterns in human-AI interactions. For example, tracking increases in opinion-sharing and other key metrics allows iterative configuration and optimization of combined teams. 💬 Transparent Communication Channels Empower Humans. Using shared platforms like Slack for all human and AI interactions ensures transparency and inclusivity. Humans can easily observe agent reasoning and intervene when necessary, while agents remain responsive to human queries. Link to paper in comments.

The Future of Teamwork is Human + AI, I just reviewed fascinating new Massachusetts Institute of Technology research by Prof Sinan Aral and Harang Ju on AI-human collaboration that has significant implications for innovation teams. Key findings from the study:- • Human-AI teams communicated 137% more than human-human teams. • Workers with AI partners focused 23% more on content generation. • Human-AI teams achieved 60% greater productivity per worker. • AI teams produced higher-quality text, while human teams created better images. • AI personality traits can be matched to complement human personalities for optimal results. Most remarkably, ads created by human-AI teams performed comparably to human-human teams in real-world tests with ~5M impressions! The researchers developed “MindMeld” - a collaboration platform enabling humans and AI agents to work together in real-time. Their field experiments revealed that AI agents reduce social coordination costs, letting humans focus more on creative output. As a builder and innovator working with agentic AI solutions, I find this research validates what I’ve experienced: the future isn’t about AI replacing humans, but about thoughtfully designing AI systems that complement human strengths. What’s your experience working with AI collaborators? Have you noticed changes in your productivity or communication patterns? #AICollaboration #FutureOfWork #AgenticAI #Innovation

Are you finding exploring generative AI tools daunting? Sharing your successes – and stumbles – with others can help it feel less so. That’s why we gathered our global Mars Corporate Affairs function last week for the latest in our practical GenAI series, this time on a very important topic - improving the quality of Gen AI prompts. From adapting communication across channels or audience styles to team haikus, it was great to hear how our teams are already experimenting with these emerging tools creatively and, importantly, safely – and we rolled up our sleeves and tried different prompting techniques together on the call. I thought I'd share a few of our key takeaways as they may be useful for others:  * Prompt quality drives AI value: Crafting clear, specific prompts significantly improves AI output quality, reduces rewrites, and increases trust in results. Investing time in prompt creation upfront is a smart way to maximize efficiency.  * There are different advanced prompting techniques: We learned about shot-based prompting (zero, one, few-shot), chain-of-thought prompting (breaking down complex tasks), and prompt-priming (setting context and tone at the start) to enhance AI performance. * Consider a ‘prompt library’: There’s an art and science to developing great prompts. Consider banking reusable prompts across teams to save time and share best practices.  * Troubleshooting: Expect issues like hallucinated data, token limits and slow responses. Consider providing ‘escape routes’ in prompts (e.g. instructing the AI to say "I don't know" if unsure).  * Last but not least, keep the human in the loop: Today AI should augment, not replace, human judgment to review, refine, and validate AI outputs for accuracy, bias, and ethical considerations. Prompting by nature is an iterative process - it's normal not to get the perfect output on the first try; iterating and refining prompts through conversation with the AI leads to better results. But our best tip by far – just get stuck in.  Experimenting and sharing your learnings (in accordance with your company's safe Gen AI guidelines) is the best way to build these new muscles more quickly. Got a favourite prompt? Or other great tips in building capabilities in this area, I’d love to hear it. Big thanks to Camilla Vasquez, Katherine Horrocks, Ishtar Schneider and many others for being a driving force in helping to build our capabilities in this important area. #GenAI #CorporateAffairs

I just built a Voice RAG Agent one that can listen, think, and talk back using your own data. Instead of typing prompts into ChatGPT, imagine being able to call an AI agent, ask a question like: “What does HIPAA say about contingency planning?” and get a clear, conversational voice answer pulled directly from your company’s documents. Here’s what powers it : 🔹 Retell AI - handles the real-time voice conversation 🔹 n8n - automates the workflow between tools 🔹 OpenAI embeddings & Pinecone - make it a true RAG system that retrieves answers from your own files Where this can be useful: – Compliance hotlines (HIPAA, SOC2, ISO, etc.) – Customer support that speaks your internal policy docs – Voice-based knowledge assistants for internal training – Product documentation helplines that talk to clients This isn’t just another chatbot it’s a voice-first AI system that learns from your content, not the public web. Watch the full tutorial below to see how it’s built step-by-step using Retell AI + n8n + OpenAI.