Complete System Architecture of AIAgent

1. Intent Layer
2. Planning Layer
3. Execution Layer
4. Tool Layer
5. Memory Layer
6. Knowledge Layer
7. Policy & Guardrail Layer
8. Observability Layer
9. Persistence Layer
10. Governance Layer

prompt = '

	You are a task classifier.
	User query: {query}
 
	Classify the user query into one of:
 
	1. simple_qa
	2. retrieval
	3. tool_call
	4. multi_step
 
	Return JSON:
	{
  		"type": "..."
	}
’

1. simple_qa → LLM direct answer

2. retrieval → call vector database for direct retrieval / call Browser retrieval

3. tool_call → enter ReAct loop
4. multi_step → enter ReAct loop

LLM decides next action
↓
Call one tool
↓
Append tool result
↓
Repeat

while True:
 
    response = llm(messages, tools=tool_schema)
 
    if response.tool_calls:
        result = execute_tool(...)
        messages.append(tool_result)
 
    else:
        break

max_steps = 15

if tool = send_mail:
	require confirmation

Unified Memory system (long-term + short-term)

1️⃣ Short-term memory (Session Memory)
	•	current task state
	•	tool invocation history
	•	intermediate decision result
2️⃣ Long-term memory (Persistent Memory)
	•	user preferences history
	•	historical task records
	•	decision result archives
	•	email sending logs
Otherwise the system cannot:
	•	track back
	•	analyze
	•	audit
	•	learn and optimize

Policy & Safety Layer (Security Controls)
Now it can send emails automatically.
Risky issues:
	•	prompt injection?
	•	user sending mass emails?
	•	sensitive info leakage?
	•	calling dangerous APIs?
A mature system must have:
Guardrails
	•	tool invocation whitelist
	•	parameter validation
	•	risk scoring
	•	human confirmation checkpoints
	•	rate limiting

Observability
Must be able to answer:
	•	Which tool fails the most?
	•	Average steps per task execution?
	•	LLM token consumption?
	•	Email success rate?
	•	Which step takes the most time?
This requires:
	•	structured logging
	•	step-level tracing
	•	metrics
	•	visual dashboards
Otherwise optimization is impossible.

Task Persistence
Must support:
	•	task interruption recovery
	•	asynchronous execution
	•	multi-user concurrency
	•	failure retry
	•	delayed execution
Requires:
	•	Redis / DB to store state
	•	unique ID for each task
	•	state machine persistence
Otherwise it's just a script, not a system.

When tools exceed 10, issues appear:
	•	tool conflicts
	•	parameter confusion
	•	incorrect routing
	•	LLM picking wrong tools
Mature systems require:
	•	tool registry
	•	tool capability descriptions
	•	tool prioritization
	•	tool invocation logs

Currently:
 Error → Program crash

Mature systems must have:
	•	automatic retry
	•	backup models
	•	fallback strategy
	•	LLM self-healing prompts

Mature systems must monitor:
	•	token usage
	•	tool invocation cost
	•	API fees
	•	model selection strategy (small models first)
Otherwise costs are uncontrollable.

When encountering:
	•	high-risk operations
	•	low confidence
	•	multiple ambiguous candidates
It must:
 Agent → request human confirmation

Otherwise it cannot be used in real business.

		┌──────────────────┐
                │   API Gateway    │
                └──────────────────┘
                          ↓
                ┌──────────────────┐
                │ Intent Router    │
                └──────────────────┘
                          ↓
                ┌──────────────────┐
                │ ReAct Engine     │
                └──────────────────┘
                          ↓
          ┌───────────────┼────────────────┐
          ↓               ↓                ↓
   Tool Layer       Knowledge Layer     Memory Layer
          ↓               ↓                ↓
                ┌──────────────────┐
                │ Policy Layer     │
                └──────────────────┘
                          ↓
                ┌──────────────────┐
                │ Persistence DB   │
                └──────────────────┘
                          ↓
                ┌──────────────────┐
                │ Observability    │
                └──────────────────┘

Signs of a mature system
•	Serving 100+ users concurrently
•	Supporting long-chain tasks
•	Interruption recovery
•	Automatic failure repair
•	Complete audit logging
•	Cost accounting
•	Security policies
•	Human approval checkpoints