How to Apply Deep Reasoning Agents in AI Solutions

Agentic AI is 𝗻𝗼𝘁 about wrapping prompts around a large language model. It’s about designing systems that can: → 𝗣𝗲𝗿𝗰𝗲𝗶𝘃𝗲 their environment → 𝗣𝗹𝗮𝗻 actionable steps → 𝗔𝗰𝘁 on those plans → 𝗟𝗲𝗮𝗿𝗻 and improve over time And yet, many teams hit a wall—not because the models fail, but because the 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 behind them isn’t built for agent behavior. If you’re building agents, you need to think in 𝗳𝗼𝘂𝗿 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀: 1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 → Agents must decompose goals into steps and execute them independently. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 → Without memory, agents forget past context. Vector DBs like FAISS, Redis, or pgvector aren’t optional—they’re foundational. 3. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 → Agents must go beyond text generation—calling APIs, browsing, writing code, and executing it. 4. 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 → The future isn’t just one agent. It's many, working together—planner-executor setups, sub-agents, role-based dynamics.     Frameworks like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻,𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗗𝗞, and 𝗖𝗿𝗲𝘄𝗔𝗜 make these architectures more accessible. But frameworks alone aren’t enough. If you’re not thinking about: • 𝗧𝗮𝘀𝗸 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 • 𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹𝗻𝗲𝘀𝘀 • 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 …your agents will likely remain shallow, brittle, and fail to scale. The future of GenAI lies in 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿, not just fine-tuning prompts. 2025 is the year we go from 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 to 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀. Let’s build agents that don’t just respond—but 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗱𝗮𝗽𝘁, 𝗮𝗻𝗱 𝗲𝘃𝗼𝗹𝘃𝗲.

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.

I just finished reading three recent papers that every Agentic AI builder should read. As we push toward truly autonomous, reasoning-capable agents, these papers offer essential insights, not just new techniques, but new assumptions about how agents should think, remember, and improve. 1. MEM1: Learning to Synergize Memory and Reasoning Link: https://bit.ly/4lo35qJ Trains agents to consolidate memory and reasoning into a single learned internal state, updated step-by-step via reinforcement learning. The context doesn’t grow, the model learns to retain only what matters. Constant memory use, faster inference, and superior long-horizon reasoning. MEM1-7B outperforms models twice its size by learning what to forget. 2. ToT-Critic: Not All Thoughts Are Worth Sharing Link: https://bit.ly/3TEgMWC A value function over thoughts. Instead of assuming all intermediate reasoning steps are useful, ToT-Critic scores and filters them, enabling agents to self-prune low-quality or misleading reasoning in real time. Higher accuracy, fewer steps, and compatibility with existing agents (Tree-of-Thoughts, scratchpad, CoT). A direct upgrade path for LLM agent pipelines. 3. PAM: Prompt-Centric Augmented Memory Link: https://bit.ly/3TAOZq3 Stores and retrieves full reasoning traces from past successful tasks. Injects them into new prompts via embedding-based retrieval. No fine-tuning, no growing context, just useful memories reused. Enables reasoning, reuse, and generalization with minimal engineering. Lightweight and compatible with closed models like GPT-4 and Claude. Together, these papers offer a blueprint for the next phase of agent development: - Don’t just chain thoughts; score them. - Don’t just store everything; learn what to remember. - Don’t always reason from scratch; reuse success. If you're building agents today, the shift is clear: move from linear pipelines to adaptive, memory-efficient loops. Introduce a thought-level value filter (like ToT-Critic) into your reasoning agents. Replace naive context accumulation with learned memory state (a la MEM1). Storing and retrieving good trajectories, prompt-first memory (PAM) is easier than it sounds. Agents shouldn’t just think, they should think better over time.

A brilliant idea isn’t a fact—until it is. Many groundbreaking discoveries seem obvious only in hindsight, once they unify a web of seemingly isolated facts into a general principle. Before we connected the dots between evolution, genetics & material science, silk was just a thread, proteins were just biological molecules, & genes were just codes. But once we saw their relationships, we unlocked deep truths about how nature builds materials at every scale. What If AI Could Think in Relationships Instead of Just Memorizing? Most AI today doesn’t work this way. It merely predicts the next token, unaware of whether its own output is meaningful, correct, or groundbreaking. They: ❌ Lack true reasoning—they do not verify if their responses make sense. ❌ Cannot correct themselves—once they generate something, they have no mechanism to reflect and refine their own ideas. ❌ Do not connect ideas deeply—they retrieve, not discover. 💡 SciAgents does something different. Rather than treating knowledge as isolated facts, it builds a massive relational graph, connecting every concept and idea to others. Then, a team of AI agents explores this graph, not just by taking the shortest path between ideas, but by wandering through unexpected links. How SciAgents Reasons over Graphs ▶️Instead of taking the shortest path between two ideas (which can be too direct & limiting), SciAgents samples diverse paths through a powerful algorithm that explores ever-growing sets of diverse waypoints. This allows it to natively explore broader, richer relationships—leading to unexpected discoveries. ▶️For example, to explore the connection between silk and energy efficiency, SciAgents didn’t just look at direct links. It uncovered intermediate concepts like biocompatibility, multifunctionality & structural coloration, revealing new ways to design bioinspired materials that human researchers might have overlooked. Why does this matter for building better AI for science and beyond? 1⃣Generalization is the key to intelligence. Memorization alone won’t get AI to true reasoning—but structuring knowledge in a relational way can. 2⃣SciAgents goes beyond predicting words. It constructs maps of ideas by conceptual blueprints, from genes encoding proteins to evolutionarily refined materials like silk, and extrapolates new designs. 3⃣It refines its own outputs. Rather than passively generating text, SciAgents’ multi-agent system debates, critiques, and improves hypotheses, making its discoveries deeper and more reliable. Graph-based reasoning plus multi-agent collaboration is not just a better way for AI to think—it’s likely on a critical path towards AGI. The ability to form deep, structured insights from sparse information is what separates mere computation from true intelligence. A. Ghafarollahi, M.J. Buehler, SciAgents: Automating Scientific Discovery Through Bioinspired Multi-Agent Intelligent Graph Reasoning, Adv. Materials, DOI: 10.1002/adma.202413523, 2025