Schedule Strategy Call
Schedule Strategy Call
Case Study
Linkedin X-twitter Facebook

Agentic AI Assistant Framework for Autonomous Task Executions

From Conversation to Action: Multi-Agent AI That Plans, Executes, and Delivers Outcomes 

Client

A product user aimed to build an intelligent AI assistant capable of going beyond basic chat responses. The requirement was to create a multi-agent AI system that could understand user intent, dynamically select tools, interact with external APIs, and autonomously complete real-world tasks such as bookings, research, and planning. 

Challenges

During analysis, we found that most booking and assistance platforms operate in silos, requiring users to manually connect multiple tools to complete a single task. 

  • Manual Search & Booking Fatigue: Users had to search reviews, compare options, and complete booking steps manually across different apps.
  • Lack of Personalization: Existing systems couldn’t adapt to specific user needs like cuisine type, time slot, or proximity.
  • Disconnected Systems: Integrating review platforms, location APIs, and booking portals required manual input and cross-referencing.
  • Poor User Experience: Switching contexts (from maps to reviews to bookings) created friction and increased user drop-offs.

These gaps highlighted the need for an autonomous, agent-driven AI assistant capable of orchestrating tools within a unified workflow. 

Our Strategy:

TechTez implemented a multi-agent orchestration framework supported by a centralized FastMCP tool server to enable intelligent, secure, and scalable task execution. 

Instead of building a single monolithic AI assistant, we designed a modular architecture where specialized agents collaborate to understand user intent, break down complex requests into structured tasks, select appropriate tools, and execute them autonomously 

Tech Stack:

  • LLMs: GPT-4o, LLaMA 3.1
  • Orchestration Framework: CrewA
  • Tool Server: FastMCP
  • Framework Layer: LangChain
  • Integration Layer: REST APIs, Google APIs, External Service Platforms
  • Core Architecture: Multi-Agent Orchestration with Centralized Tool Execution

Use Cases Implemented:

Restaurant Booking Assistant

  • Processes inputs such as cuisine, time, and group size
  • Uses sentiment analysis to identify top-rated restaurants
  • Integrates Google APIs and booking platforms (Zomato, Swiggy, Dineout)
  • Completes reservations autonomously and Sends confirmations to users

Outcome: End-to-end automated booking workflow from a single user prompt.

General Assistance Agent

  • Handles travel research, comparisons, budgeting, and planning
  • Executes fully autonomous multi-step workflows
  • Gathers real-time information via APIs
  • Delivers complete, useful, human-like responses

Outcome: Conversational query transformed into structured, actionable execution.​

Technical Approach:

  • LLM-Powered Intent Interpretation: Using GPT-4o-mini, the system interprets diverse natural language queries and generates structured reasoning pipelines.
  • Tool-Oriented Architecture: The assistant operates as a toolbox of functions and services, dynamically assembling tool-augmented workflows.
  • Multi-Agent Orchestration: CrewAI agents coordinate planning, execution, and response generation, enabling intelligent task decomposition.
  • Centralized Tool Server (FastMCP): All tools and services are managed via a centralized server to ensure secure, scalable execution.
  • Live API Integration: The system fetches real-world data through external APIs and platforms to complete tasks autonomously.
  • Structured Output Generation:The assistant returns context-aware, structured, and actionable responses rather than plain text replies.

Results & Impact

The solution delivered measurable efficiency, accuracy, and scalability improvements.

  • 100% automation from user query to restaurant booking no manual steps required
  • 70% reduction in average search-to-booking time during internal trials
  • Increased booking success rate by 25%, thanks to intelligent sentiment filtering and real-time availability matching
  • Improved user satisfaction scores in test sessions (measured via feedback survey)
  • Validated the real-world potential of Agentic AI for transforming customer-facing workflows
  • Built scalable foundation for expanding to 50+ cities and multiple food platforms
  • Reduced manual intervention in booking and planning workflows
  • Scalable framework adaptable to multiple business use cases
  • Accelerated deployment of agentic AI solutions
  • Enabled AI-driven autonomous task execution

Our Thought Leadership Guides

  • Case Study

GPLB Technical Requirements: What UK Communication Providers must build to Comply

A clear breakdown of GPLB technical requirements: TOTSCo's envelope, letterbox and directory APIs, security options, error codes, and testing steps CPs need before going live.
  • Case Study

TOTSCo Migration Mistakes UK Providers Must Avoid

TOTSCo migration is more than simply connecting to the Hub it requires complete operational readiness. This blog highlights. 7 common migration mistakes, from poor onboarding data and late connection decisions to incomplete testing and weak exception handling. It explains why transaction visibility, monitoring, clear ownership, and recovery processes are essential before going live.
Common TOTSCo migration mistakes UK Communication Providers should avoid.
  • Case Study

GPLB Switching: A Practical Guide for UK CPs

GPLB switching is the UK framework for business broadband switching via Tosco. Learn how it works, why it matters, and how to simplify integration.
GPLB Switching guide for UK Communications Providers, highlighting TOTSCo integration and managed switching automation.