Adora Nwodo

In my recent interview with UOL, I talked about how AI agents are transforming the technology landscape, and how Brazil is innovating in data and AI. Companies like iFood and PicPay are reimagining their businesses, enabled by AI and the Cloud. I'm particularly excited about the era of AI agents - autonomous systems that can reason, plan, and adapt to achieve specific goals. We're seeing this revolution unfold across search, transactions, and enterprise applications. What's most encouraging is how these technologies are already delivering real business value, with proper governance and human oversight remaining central to successful implementation. Looking forward to connecting with Brazil's vibrant tech community and exploring how we can continue pushing boundaries together in AI innovation. See you at the Summit! #Letsgo https://lnkd.in/eYNaRH6c

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Exploring the LLM Ecosystem: Key Insights 🌐 The LLM Ecosystem is transforming industries with powerful AI models and tools. Here's a quick breakdown: 1️⃣ Available Large Language Models GPT-4 (OpenAI): Leading in conversational AI. PaLM (Google): Known for reasoning and cross-lingual capabilities. Claude (Anthropic): Prioritizing safety and ethical alignment. LLaMA (Meta) & Mistral: Efficient and open-weight models. 2️⃣ General Use Cases Customer Service: AI chatbots for support. Content Creation: Automated writing for blogs, reports, and more. Code Assistance: Tools for coding help and debugging. Language Translation: Real-time cross-language communication. Healthcare: AI-powered clinical support and research. 3️⃣ Specific Implementations Sales & Marketing: Content generation and lead scoring. Legal Tech: Document review and contract analysis. Education: Personalized learning and tutoring. Finance: Automating reports and customer interactions. 4️⃣ Tools & UIs APIs & SDKs: Easy integration via platforms like OpenAI. UI Platforms: Streamlit and Gradio for user-friendly LLM apps. Fine-Tuning: Hugging Face for model customization. The LLM ecosystem is unlocking opportunities for businesses and developers, driving innovation across sectors. Follow the AI Ka Doctor (Free AI & Data Science Resources)  channel on WhatsApp: https://lnkd.in/dCTCEKKc Follow Habib Shaikh For more such content.

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𝗛𝗼𝘄 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝗠𝗮𝗻𝗮𝗴𝗲 𝗦𝗲𝘀𝘀𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 Managing sessions in LLMs isn’t just about keeping track of prompts. It is about orchestrating memory, context, and tools seamlessly across multiple agents. That’s where MCP servers come in. Here’s the flow I captured visually → Context Window as Working Memory: Holds system prompt, user inputs, assistant responses, tool calls, and rolling context → MCP Servers: Each server maintains its own session state, ensuring consistency across interactions → System Prompt + Tools + Code Loops: Work together to power agentic behavior while grounding outputs in real tasks → LLM Integration: The context is constantly summarized and updated before being sent to the model This structure ensures scalable, multi-session orchestration, which is critical for enterprise AI where multiple tasks and tools interact simultaneously.

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Continuing the development of BDB 10.5, the focus is on enhancing AI Agents, particularly Data Agents, introduced in v10.0. These autonomous software systems are pivotal in perceiving environments, making decisions, and achieving set goals, often utilizing large language models and diverse tools. Improving Data Agents is key to enhancing BDB's data-centric capabilities. Here's how we plan to elevate their functionality: - Specialized Agent Design: Tailoring data agents for specific domains or use cases like "Sales Data Analyst Agent" ensures optimized instructions, targeted data sources, and domain-specific terminology for heightened accuracy and relevance. - Clear & Focused Instructions: Implementing precise agent-level and data source-specific instructions enhances performance. - Schema Understanding & Data Source Scope Optimization: Ensuring agents interpret data sources accurately, with clear and descriptive names for tables and columns. - Iterative Improvement Process: Establishing a continuous enhancement loop for data agents involves benchmarking, diagnosing errors, providing targeted examples, and addressing join complexities. Introducing Multi-Agent Orchestration will empower the BDB Platform to handle intricate, multi-step tasks through coordinated specialized AI agents. The architecture design includes centralized orchestration, decentralized coordination, hierarchical structures, communication protocols, and workflow definition for effective task delegation. In terms of Security and Observability for AI Agents, we adhere to best practices such as input validation, least privilege, encryption, secrets management, and a zero-trust approach. Observability strategies encompass comprehensive logging, real-time monitoring, tracing, bias detection, and model drift analysis. Additionally, Agent Performance Dashboards offer clear visualizations of performance metrics, facilitating continuous optimization and bottleneck identification. #AI #DataAgents #MultiAgentOrchestration #Security #Observability #BDBPlatform

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Is Your Infrastructure Ready for AI Agents? The truth : Most enterprise stacks aren’t built for this. Traditional infrastructure was never designed for the dynamic, asynchronous, and resource-diverse needs of AI agents. It's GPU-hungry at times, latency-sensitive at others, and increasingly multi-modal. AI agents aren’t just one big model they’re orchestras of SLMs, prompt routers, vector search, KG, memory modules, and decision engines, all working in parallel. 🔄 One moment you’re routing tasks to a lightweight planning agent on a CPU. The next, you’re invoking a 70B parameter model needing H100s. Static infrastructure? It breaks. Homogeneous design? Too expensive. Siloed resources? Slows everything down. What’s needed is an infrastructure that adapts as fluidly as the agents it supports, we need to Rethink Infrastructure with AI Agents in Mind Heterogeneous architecture is non-negotiable. 1. Blend CPUs, low-end GPUs, and high-end accelerators based on workload needs. 2. Storage and networking must be agent-aware. Agents need shared memory, fast I/O, and real-time context syncing. 3. Elastic, event-driven provisioning. Let agents wake up infra when needed and shut it down when done. 4. Observability with context, not chaos. Understand how agents think and act, not just when they fail. This isn’t just about scale. It’s about smart scaling making infrastructure as intelligent and composable as the agents it serves. 
So ask yourself: Is your infrastructure just cloud-ready? Or is it agent-ready?

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