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Module 08 · Page 9 of 20 · 8 sections

Agentic AI System Design

Designing a system around an agent: orchestrator, tools, LLM and memory, the plan-act-observe-reflect loop and what to consider.

07RAG System Design09AI Agents Architecture10System Components Deep Dive
8.1

What is Agentic AI?

  • AI systems that can autonomously plan, reason, take actions, use tools and achieve goals with minimal human intervention.
  • It combines LLM capabilities with planning, memory, tool usage and decision making to solve complex real-world tasks.
  • Unlike a traditional LLM application, an agent can act, observe, reflect and iterate to complete a goal.
8.2

Key Characteristics

  1. 1Autonomous decision making.
  2. 2Can use external tools and APIs.
  3. 3Able to plan and break down complex tasks.
  4. 4Maintains memory and context.
  5. 5Can learn from feedback and adapt.
  6. 6Works in iterative loops (plan, act, observe).
  7. 7Can handle multi-step and long running tasks.
  8. 8Goal oriented and outcome driven.
8.3

High Level Architecture

UserGoal / query
Agent OrchestratorPlan, manage state, call tools
Tools / External SystemsVector DB, search, APIs, code, email
LLM (Reasoning & Planning)Plan steps, generate actions
MemoryHistory, task state, vector DB
The orchestrator drives an LLM, tools and memory in a loop: observe results, reflect, plan the next step.
8.4

1. Core Components

1
Planner
Breaks down the goal into smaller steps.
2
LLM
Reasoning, decision making and generation.
3
Tools
External systems like search, database and APIs.
4
Memory
Stores conversation, task state and knowledge.
5
Executor
Executes actions using selected tools.
6
Observability
Logs, monitoring and evaluation.
Related09AI Agents Architecture
8.5

2. Agent Workflow (Plan, Act, Observe, Reflect)

PlanUnderstand the goal and break it into steps
ActUse tools to execute the current step
ObserveGet results from tools and environment
ReflectEvaluate results, update memory, plan next step
Repeat until the goal is met
8.6

3. Types of Agents

1
Simple Reflex Agent
Basic rule-based actions.
2
ReAct Agent
Reasoning plus acting using an LLM.
3
Tool-Use Agent
Uses external tools (APIs, DB).
4
Planning Agent
Creates a multi-step plan before acting.
5
Multi-Agent System
Multiple agents collaborate.
6
Autonomous Agent
Works independently for long tasks.
8.7

4. Example Use Cases

1
Research Assistant
Search, summarize and compile reports.
2
Code Assistant
Read docs, write and run code.
3
Data Analyst Agent
Query databases and analyze data.
4
Customer Support Agent
Answer queries using tools.
5
Autonomous Task Agent
Handle end-to-end tasks.
6
Enterprise Automation
Integrate with internal tools.
8.8

5. Design Considerations

  1. 1Tool selection and integration.
  2. 2Memory management (short-term and long-term).
  3. 3Error handling and self-correction.
  4. 4Security and access control.
  5. 5Monitoring, logging and evaluation.
  6. 6Cost optimization.
  7. 7Human-in-the-loop for critical tasks.
  8. 8Scalability for multi-agent systems.
Related11Reliability, Fault Tolerance and High Availability12Security, Privacy and Responsible AI13Monitoring, Logging and Observability