Key Elements of AI Agents

I have felt a tipping point with #AI Agents lately... Background agents in Cursor, OpenAI’s Codex, and agents in Linear make it feel like agents truly live alongside me and my team. It's not just the capabilities of the agents that matter, it’s where they “live.“ Last week I was able to initiate several small product changes directly from Slack, just by sending a DM to Cursor. Collaborating with agents like this feels effortless, because they're no longer something you have to “decide” to use... they’re integrated into the tools you already have. This is so important, because no matter how great an agent is, if it’s not supported by smart people, it can’t be successful. Agents are powerful, but lack judgment; people have refined taste and understand nuance, but they lack bandwidth. Successful products create a “feedback loop” between the two parties, so they can continually dance towards the ideal solution. Agents need an ergonomic digital workspace where they can collaborate with people. That means... - It's easy for people to delegate real work to agents. - When agents need input or get blocked, it's easy for them to get help. - It's easy for people to see what's going on with their agents and understand their reuslts - Agents and people both have access to all the right data so they can make good decisions – both in the tool and from third party sources. We are building Element451 to be that digital workspace for higher ed – a true ”AI Workforce Platform“ where it is easy for agents and people to work together. For me this is an exciting new opportunity to apply the idea of “Digital Hospitality”. Not only is our platform a place we welcome customers, it’s a home for our agents as well. When we think of agents like teammates, it becomes clearer the resources, access, and capabilities they need to be successful. And when agents succeed, customers win. I put together some thoughts about what it means to be an AI Workforce for our blog. Whether or not you work in higher ed, I hope it can help you understand the dramatic shift that’s underway, and how AI Workforce concepts can help you thrive in this new world! https://lnkd.in/ehv7nWqs

If you’re an AI engineer, here are the 15 components of agentic AI you should know. Building truly agentic systems goes far beyond chaining prompts or wiring tools. It requires modular intelligence that can perceive, plan, act, learn, and adapt across dynamic environments - autonomously and reliably. This framework breaks it down into 15 technical components: 🔴 1. Goal Formulation → Agents must define explicit objectives, decompose them into subgoals, prioritize execution, and adapt dynamically as new context arises. 🟣 2. Perception → Real-time sensing across modalities (text, visual, audio, sensors) with uncertainty estimation and context grounding. 🟠 3. Cognition & Reasoning → From world modeling to causal inference, agents need inductive, abductive reasoning, planning, and introspection via structured knowledge (graphs, ontologies). 🔴 4. Action Selection & Execution → This includes policy learning, planning, trial-and-error correction, and UI/tool interfacing to interact with real systems. 🟣 5. Autonomy & Self-Governance → Independence from human-in-the-loop oversight through constraint-aware, initiative-taking decision frameworks. 🟠 6. Learning & Adaptation → Support for continual learning, transfer learning, and meta-learning with feedback-driven self-improvement loops. 🔴 7. Memory & State Management → Episodic memory, working memory buffers, and semantic grounding for contextually-aware actions over time. 🟣 8. Interaction & Communication → Natural language generation and understanding, negotiation, and multi-agent coordination with social signal processing. 🟠 9. Monitoring & Self-Evaluation → Agents should monitor their own performance, detect anomalies, benchmark against goals, and recover autonomously. 🔴 10. Ethical and Safety Control → Safety constraints, transparency, explainability, and alignment to human values - non-negotiable for real-world deployment. 🟣 11. Resource Management → Optimizing compute, memory, and energy with intelligent resource scheduling and infrastructure-aware orchestration. 🟠 12. Persistence & Continuity → Agents must preserve goal state across sessions, maintain behavioral consistency, and recover from disruptions. 🔴 13. Agency Integration Layer → Modular architecture, orchestration of internal components, and hierarchical control systems for scalable design. 🟣 14. Meta-Agent Capabilities → Delegation to sub-agents, participation in agent collectives, and orchestration of agent teams with diverse roles. 🟠 15. Interface & Environment Adaptability → Adaptation across domains and tools with robust APIs and reconfigurable sensing-actuation layers. 〰️〰️〰️ 🔁 Save and share this if you’re designing agents beyond the demo stage. 🔔 Follow me (Aishwarya Srinivasan) for more data & AI insights

Now that you’ve selected your use case, designing AI agents is not about finding the perfect configuration but making deliberate trade-offs based on your product’s goals and constraints. You’ll be optimizing for control, latency, scalability, or safety, and each architectural choice will impact downstream behavior. This framework outlines 15 of the most critical trade-offs in Agentic AI to help you build successfully: 1.🔸Autonomy vs Control Giving agents more autonomy increases flexibility, but reduces human oversight and predictability. 2.🔸Speed vs Accuracy Faster responses often come at the cost of precision and deeper reasoning. 3.🔸Modularity vs Cohesion Modular agents are easier to scale. Cohesive ones reduce communication overhead. 4.🔸Reactivity vs Proactivity Reactive agents wait for input. Proactive ones take initiative, sometimes without clear triggers. 5.🔸Security vs Openness Opening up tool access increases capability, but also the risk of data leaks or misuse. 6.🔸Memory Depth vs Freshness Deep memory helps with long-term context. Fresh memory improves agility and faster decision-making. 7.🔸Multi-Agent vs Solo Agent Multi-agent systems bring specialization but add complexity. Solo agents are easier to manage. 8.🔸Cost vs Performance More capable agents require more tokens, tools, and compute, raising operational costs. 9.🔸Tool Access vs Safety Letting agents access APIs boosts functionality but can lead to unintended outcomes. 10.🔸Human-in-the-Loop vs Full Automation Humans add oversight but slow things down. Full automation scales well but may go off-track. 11.🔸Model-Centric vs Function-Centric Model-based reasoning is flexible but slower. Function calls are faster and more predictable. 12.🔸Evaluation Simplicity vs Real-World Alignment Testing in a sandbox is easier. Real-world tasks are messier, but more meaningful. 13.🔸Static Prompting vs Dynamic Planning Static prompts are stable. Dynamic planning adapts better, but adds complexity. 14.🔸Generality vs Specialization General agents handle a wide range of tasks. Specialized agents perform better at specific goals. 15.🔸Local vs Cloud Execution Cloud offers scalability. Local execution gives more privacy and lower latency. These kinds of decisions shape results of your AI system, for better… or worse. Save this for reference and share with others. #aiagents #artificialintelligence

What actually makes an AI “agentic”? It’s not just a chatbot with a fancy wrapper. It’s an autonomous system that can reason, plan, and act—on its own. But building one? That’s where it gets complex. I came across a brilliant breakdown of the 10 core components of Agentic AI, and it’s one of the most practical frameworks I’ve seen so far. If you’re building or evaluating AI agents, this is a must-read. Here’s a quick look at what’s covered: Experience Layer – where users interact (chat, voice, apps) Discovery Layer – retrieves context using RAG, vector DBs Memory Layer – stores episodic, semantic, long-term memory Reasoning & Planning – uses CoT, ToT, ReAct to make decisions Agent Composition – modular agents collaborating as teams Tool Layer – actually executes code, APIs, SQL, etc. Feedback Layer – learns from outcomes via evaluation loops Infrastructure – handles deployment, scaling, versioning Multi-Agent Coordination – planner-worker patterns in action Observability – monitors memory, tools, decisions in real-time The stack is evolving fast. And frameworks like LangGraph, CrewAI, AutoGen, and LangChain are leading the charge. Would love to hear: which layer do you think is the hardest to get right?