Agent Requirements

Agent Objective:

• Core Task: Autonomously resolve Tier-1 and Tier-2 customer support inquiries (e.g., FAQs, order tracking, returns) by grounding responses in a verified Knowledge Base (KB) and executing safe external API actions.
• Success Criteria: High deflection rate, low latency (under 3s per turn), and zero unauthorized or duplicated transactions.

Autonomy & Constraints:

• Autonomy: The agent operates with supervised autonomy. It can retrieve data and execute read-only actions freely but requires human-in-the-loop (HITL) confirmation for destructive or high-value actions (e.g., refunds over $50).
• Constraints: Must adhere strictly to predefined SOPs. Escalate immediately to a human agent when user sentiment is highly negative or when a query falls outside its defined domain.

Tool & Action Design

To ensure Reliability and Fault Tolerance, the agent relies on highly decoupled, well-defined tools:

• KB_Search_Tool: Vector database query tool with hybrid search (semantic + keyword) to fetch relevant SOPs and articles.
• Order_Management_Tool: Interfaces with the backend e-commerce system.
• Idempotency Handling (Edge Case): For actions like Issue_Refund, the tool design mandates an idempotency key (e.g., hash(user_id + order_id + action_timestamp)). If the agent retries a failed network request, the backend detects the duplicate key and prevents double-refunding, ensuring state consistency.

Agent Architecture

The architecture is built for Scalability and Availability, deploying stateless microservices across multiple Availability Zones (AZs) behind a load balancer.

• API Gateway: Routes requests, terminates SSL, and handles authentication.
• Orchestrator Engine: Scalable Kubernetes pods running the main LLM control loop. Being stateless, these can auto-scale horizontally based on traffic spikes.
• Resilience & Fault Tolerance: External API calls to the LLM provider and backend services are wrapped in Circuit Breakers. If the primary LLM fails, the system automatically falls back to a secondary provider or routes immediately to the human queue.

Memory & Context Design

To maintain Performance, memory is tiered:

• Short-Term Memory (Session): Stored in Redis clusters. Maintains the sliding window of the current conversation context. Fast read/write times ensure low latency per response turn.
• Long-Term Memory (Persistent): Summarized user profiles and historical interactions stored in PostgreSQL. Fetched only on session initialization.

Core Reasoning

The agent uses a ReAct (Reasoning and Acting) framework combined with a Plan-and-Solve approach.

• Self-Correction: If a tool returns an error (e.g., "Order not found"), the agent's observation loop catches this, allowing it to re-prompt the user for a correct order ID rather than crashing.
• Performance Optimization: Semantic caching is used to intercept frequent, identical queries (e.g., "How do I reset my password?") to bypass the LLM entirely, saving latency and compute costs.

Safety & Permissions

• Guardrails Layer: A separate, smaller LLM or rule-based classifier analyzes both user inputs (for prompt injection) and agent outputs (for PII leakage or toxic content).
• Rate Limit Case (Edge Case): Implemented at the API Gateway via a Redis-backed Token Bucket algorithm. If a malicious user or bot spams requests, the gateway intercepts them, returning a 429 Too Many Requests error. This protects the backend systems from overload and manages expensive LLM token usage.