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Rahul Agarwal
Founder | Agentic AI... • 2m
Everyone wants AI agents. Very few understand the fundamentals behind them. Many teams imagine digital workers completing entire workflows automatically with almost zero oversight. Sounds powerful. Sometimes unrealistic. Remove the hype for a moment. What people call “agentic AI” is really a collection of system design patterns working together. No magic here. Just architecture decisions. If you’re building intelligent systems today, these concepts form the real foundation. Start with the core. An agent is simply software pursuing goals, taking actions, then adjusting behavior based on outcomes. Next comes tool access, where models interact with APIs, search engines, databases, or custom code. Work rarely happens instantly. Agents rely on planning, breaking objectives into smaller steps that can be executed sequentially. Then comes reasoning, comparing options and deciding the next logical move during execution. Context matters a lot. Memory systems allow agents to retain useful information across tasks and interactions. Systems also need limits. Guardrails define rules preventing unsafe behavior or harmful outputs. Humans still matter. A human-in-the-loop review step often protects critical decisions in production workflows. Information must come from somewhere. Retrieval pipelines fetch external knowledge so responses stay grounded in real data. Models also have limits. The context window determines how much information the system can process simultaneously. Workflows constantly change. The system’s state tracks progress across tasks, decisions, and environments. Performance must be measured. Evaluation frameworks track accuracy, reliability, and behavioral consistency over time. Multiple components rarely run alone. Orchestration layers coordinate agents, tools, and workflows into a coherent system. Autonomy varies widely. Some systems remain tightly controlled while others operate with increasing independence. Everything runs in cycles. Agents repeatedly observe situations, decide actions, execute tasks, and review outcomes. Visibility is essential. Observability tools reveal what the agent did, why it acted, and how well it performed. When these capabilities combine, agents start to look intelligent. And I believe the one thing fundamental to all of this is system design.
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