How to Improve Agent Intelligence

We’re witnessing a shift from static models to 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝘁𝗵𝗶𝗻𝗸, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁—not just respond. But with so many disciplines converging—LLMs, orchestration, memory, planning—how do you 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 to master it all? Here’s a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 to navigate the Agentic AI landscape, designed for developers and builders who want to go beyond surface-level hype: ↳ 𝟭. 𝗥𝗲𝘁𝗵𝗶𝗻𝗸 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: Move from model outputs to goal-driven autonomy. Understand where Agentic AI fits in the automation stack. ↳ 𝟮. 𝗚𝗿𝗼𝘂𝗻𝗱 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗶𝗻 𝗔𝗜/𝗠𝗟 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Before agents, there’s learning—deep learning, reinforcement learning, and the theories powering adaptive behavior. ↳ 𝟯. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Dive into 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜—frameworks enabling coordination, planning, and tool use. ↳ 𝟰. 𝗚𝗼 𝗗𝗲𝗲𝗽 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝘀: Learn how tokenization, embeddings, and memory management drive better reasoning. ↳𝟱. 𝗦𝘁𝘂𝗱𝘆 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Agents aren’t lone wolves—they negotiate, delegate, and synchronize in distributed workflows. ↳𝟲. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 + 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: Understand how 𝗥𝗔𝗚, vector stores, and semantic indexing turn short-term chatbots into long-term thinkers. ↳𝟳. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗦𝗸𝗶𝗹𝗹: Build agents with layered planning, feedback loops, and reinforcement-based self-improvement. ↳𝟴. 𝗠𝗮𝗸𝗲 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗗𝘆𝗻𝗮𝗺𝗶𝗰: From few-shot to chain-of-thought, prompt engineering is the new compiler—learn to wield it with intention. ↳𝟵. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 + 𝗦𝗲𝗹𝗳-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Agents that improve themselves aren’t science fiction—they're built on adaptive loops and human feedback. ↳𝟭𝟬. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Master hybrid search and scalable retrieval pipelines for real-time, context-rich AI. ↳𝟭𝟭. 𝗧𝗵𝗶𝗻𝗸 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗗𝗲𝗺𝗼𝘀: Production-ready agents need low latency, monitoring, and integration into business workflows. 𝟭𝟮. 𝗔𝗽𝗽𝗹𝘆 𝘄𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: From copilots to autonomous research assistants—Agentic AI is already solving real problems in the wild. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗼𝘂𝘁𝗽𝘂𝘁𝘀—𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹, 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. If you're serious about building the next wave of intelligent systems, this roadmap is your compass. Curious—what part of this roadmap are you diving into right now?

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

Context-aware agents require deliberate architecture that combines retrieval-augmented generation, session memory, and adaptive reasoning. This 10-step framework begins with defining the agent’s domain, use cases, and output structure, followed by ingestion and chunking of trustworthy data aligned to safety and alignment principles. Embeddings are then generated using models like OpenAI or Cohere and stored in vector databases such as FAISS or Pinecone for efficient semantic retrieval. Retrieval logic leverages k-NN search to fetch relevant chunks based on similarity and metadata filters. Prompts are engineered dynamically using retrieved context, optionally enriched with few-shot examples, and sent to LLMs like GPT-4 or Claude with configurable parameters. Session memory can be integrated to track interaction history and enhance continuity. Continuous evaluation identifies hallucinations, prompt failures, and edge cases for iterative refinement. Deployment involves wrapping the agent in an API or interface with monitoring hooks, and expansion includes tool use, personalization, and self-corrective mechanisms. If you follow this framework, you’ll be building the pipeline forming the backbone of production-grade AI agents that reason with context and respond with precision. Go build! #genai #aiagent #artificialintelligence

Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing! Happy agenting! 🤖

Ilya Sutskever explains a lot of obscure concepts, but this one will drive AI capabilities from linear improvement, to exponential. Most AI labs use agentic platforms to improve models faster than data alone. Here’s how it works. Simple agentic platforms provide access to prebuilt apps and existing curated data sources. In the self-improvement paradigm, new agents are added to build new apps and generate new data sources. 1️⃣ During model training, agents are tasked with identifying training gaps. 2️⃣ They hand those gaps to a prescriptive agent that guesses what tools or datasets will help fill each gap. 3️⃣ App builder and synthetic data agents deliver the proposed training environment. 4️⃣ The training gap agent assesses the model to see if the training gap is narrowing based on the improvement plan. If it isn’t, the cycle repeats itself. The goal isn’t to a single model, but to improve all agents to the point where each does its job effectively. The training environment (or playground) grows to host a massive app and dataset suite. In phase 2, the goal shifts from improving the playground to improving the models’ ability to self-improve. Simply put, the objective shifts from optimizing the playground to optimizing how models use the playground to improve. In phase 3, models are optimized to pass on what they learn. Optimized teacher models deliver the biggest jumps in model capabilities, but are least understood. Near-term AI capabilities were overstated, but long-term AI capabilities are underestimated. Models teaching models and models that self-improve, will accelerate skills, capabilities, and eventually, expertise development. #ArtificialIntelligence #GenAI