The Core Components of AI Agent Architecture
An AI agent is more than a model that answers questions. It is a structure that connects multiple steps and executes them to achieve a goal, and that is why agents require careful architectural design. This chapter summarizes the components you need to consider, from a practical standpoint, when designing an agent.
1. Goal
An agent always needs clarity on "what must be finished." Concrete goals like "analyze this month's sales data and write a report," "classify customer inquiries and generate draft responses," or "plan a business trip and register it on the calendar" are required.
When the goal is vague, the scope of the task keeps shifting, so the following must be made concrete at the design stage:
- What is the final deliverable?
- What does success look like?
- What input data is needed?
2. Memory
An agent stores information to continue working across steps. Memory is divided into two levels.
Short-term memory is information maintained only during the current task. This includes the report draft already written, the results of calculations from a previous step, and the content of the immediately preceding conversation.
Long-term memory is information that needs to be referenced repeatedly. Company policies, frequently used templates, customer preferences, and records of past projects fall into this category. Without long-term memory, the same explanations and configurations must be repeated every single time.
3. Planning
A typical conversational AI responds to a question immediately. But an agent breaks a task down into steps and executes them in sequence. For example, when a request for "write a market analysis report" comes in, the agent decomposes it as follows:
- Define the list of data needed
- Collect data
- Analyze
- Design the structure
- Write a draft
- Review
Without this kind of step structuring, errors increase on complex tasks.
4. External System Integration and Tool Use
The defining feature of an agent is that it connects to external systems. Representative examples include:
- Web search (Google, Bing, etc.)
- Database queries (internal DB, CRM, etc.)
- Email sending (Gmail, Outlook, etc.)
- Messaging (Slack, MS Teams, etc.)
- Calendar registration (Google Calendar, Outlook, etc.)
- Code execution (Python scripts, SQL queries, etc.)
Agents must be able to integrate with these systems safely through standard protocols like MCP. Without tool use, an agent easily becomes nothing more than a text generator. Tool use is what lets an agent produce genuinely useful results in actual work.
5. Execution and Iteration
An agent does not generate all results in a single pass. It typically moves according to the following structure:
- Execute
- Check results
- Revise if necessary
- Re-execute
6. Validation
The validation stage checks for:
- Calculation errors
- Whether conditions are met
- Policy violations
- Missing items
An agent without validation is fast but unstable. In areas where accuracy is critical, such as finance, legal, and policy documents, validation is an essential step.
7. Feedback and Improvement
An agent cannot be perfect. Therefore, the following feedback loop is needed:
- Log what humans corrected
- Analyze the types of requests that failed
- Update prompts and rules
- Adjust routing conditions
This feedback process is what keeps an agent's quality at a high level.
As we have seen, an AI agent is not a simple answer generator. It is a complex system that connects multiple steps to achieve a goal. Building a successful agent in practice therefore requires careful consideration of each of these components at the design stage.
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