Agent Architectures: Reactive, Deliberative, and Hybrid

AI agents and agentic workflows represent a fundamental paradigm shift away from stateless, request-response systems. Where traditional machine learning models execute inference in isolation—receiving an input and returning an output with no continuity—agents are autonomous, goal-oriented entities designed to reason, plan, and act iteratively over time.

Agentic workflows orchestrate the full cycle of intelligent decision-making, managing memory (context), reasoning loops, tool integration, and error recovery across multiple steps. This architecture enables agents to gather information, evaluate options, execute actions, observe outcomes, and dynamically adjust their strategy in response to new findings.

Without this architecture, systems cannot handle the demands of real-world problems: ambiguous goals, multi-step problem decomposition, or recovery from failed actions. A customer service agent, for example, cannot simply classify a ticket and stop; it must research the issue, call APIs to check account status, decide whether escalation is needed, and iteratively refine its response based on each new piece of information.

The core engineering challenge is designing agents that reason reliably, maintain coherent goals across sessions, handle tool failures gracefully, and scale to production complexity. This lesson covers the mechanisms that enable agents to persist state, coordinate multiple reasoning cycles, integrate external tools safely, and optimize for both latency and accuracy in real deployments.