Agent-Based Scheduling System

Project Snapshot

  • Category: Generative AI / Agentic Systems
  • Focus: Agent orchestration, workflow control, scheduling automation
  • Architecture: Event-driven, ReAct-based, tool-augmented agents
  • GitHub: Agent-Based Scheduling System

Executive Summary

Agent-Based Scheduling System is a production-oriented, agentic workflow designed to automate doctor discovery and appointment coordination using structured reasoning and controlled tool execution. Unlike traditional rule-based scheduling pipelines, the system dynamically interprets user intent, selects appropriate actions, and produces traceable side effects through an event-driven agent architecture.

Problem Statement

Scheduling workflows in operational systems are often rigid and brittle, leading to:

  • Inability to handle ambiguous or multi-intent user requests
  • Hard-coded decision logic that does not generalize well
  • Lack of transparency into automated decisions
  • Poor observability and limited evaluation of system behavior

Solution Overview

I designed this system as an agent-based, event-driven workflow that separates reasoning from action, enabling flexible decision-making while maintaining full control over side effects.

  • LLM-based intent detection and decision-making using the ReAct pattern
  • Semantic doctor search via vector embeddings and retrieval
  • Explicit tool invocation for scheduling actions
  • Deterministic execution with safe termination guarantees

Architecture & Approach

  • Implemented using LlamaIndex event-driven workflows
  • Explicit workflow events (prep, input, tool call, stop)
  • Clear separation between LLM reasoning and state-changing actions
  • Structured agent traces for observability and debugging
  • Evaluation hooks integrated directly into the agent lifecycle

Key Capabilities

• Event-Driven Agent Workflow

Uses explicit workflow events to control execution flow, prevent runaway loops, and ensure deterministic termination.

• Semantic Doctor Discovery

Performs embedding-based semantic search over structured doctor profiles instead of relying on keyword matching.

• Controlled Tool Execution

All state changes (scheduling requests) are executed through explicitly defined tools, ensuring safety and auditability.

• Agent Traces & Evaluation

Logs intent detection, tool selection, execution outcomes, and latency, enabling behavior-based evaluation and monitoring.

Impact & Outcomes

  • Demonstrated reliable agentic scheduling without hard-coded flows
  • Improved transparency through structured decision traces
  • Enabled evaluation of agent behavior beyond text-based metrics
  • Created a reusable blueprint for workflow-driven AI automation

Tech Stack

Languages: Python
Agent Framework: LlamaIndex (Workflows, ReAct)
LLMs: Groq-hosted LLaMA models
Embeddings: HuggingFace (BGE family)
API: FastAPI
Deployment: Docker
Evaluation: Intent accuracy, tool selection accuracy, task completion, latency