AI Agent Development Process
New Agent Request & Requirements Analysis
New agent request is submitted. Requirements are analyzed to determine business objectives, technical specifications, and integration needs.
Key Activities:
- Business case validation and ROI assessment
- Technical feasibility analysis
- Identify existing agent templates that can be reused
- Define success metrics and acceptance criteria
TMA AI Agent Development Factory Utilized:
New Agent Request & Requirements Analysis
New agent request is submitted. Requirements are analyzed to determine business objectives, technical specifications, and integration needs.
Key Activities:
- Business case validation and ROI assessment
- Technical feasibility analysis
- Identify existing agent templates that can be reused
- Define success metrics and acceptance criteria
TMA AI Agent Development Factory Utilized:
New Agent Request & Requirements Analysis
New agent request is submitted. Requirements are analyzed to determine business objectives, technical specifications, and integration needs.
Key Activities:
- Business case validation and ROI assessment
- Technical feasibility analysis
- Identify existing agent templates that can be reused
- Define success metrics and acceptance criteria
TMA AI Agent Development Factory Utilized:
New Agent Request & Requirements Analysis
New agent request is submitted. Requirements are analyzed to determine business objectives, technical specifications, and integration needs.
Key Activities:
- Business case validation and ROI assessment
- Technical feasibility analysis
- Identify existing agent templates that can be reused
- Define success metrics and acceptance criteria
TMA AI Agent Development Factory Utilized:
New Agent Request & Requirements Analysis
New agent request is submitted. Requirements are analyzed to determine business objectives, technical specifications, and integration needs.
Key Activities:
- Business case validation and ROI assessment
- Technical feasibility analysis
- Identify existing agent templates that can be reused
- Define success metrics and acceptance criteria
TMA AI Agent Development Factory Utilized:
New Agent Request & Requirements Analysis
New agent request is submitted. Requirements are analyzed to determine business objectives, technical specifications, and integration needs.
Key Activities:
- Business case validation and ROI assessment
- Technical feasibility analysis
- Identify existing agent templates that can be reused
- Define success metrics and acceptance criteria
TMA AI Agent Development Factory Utilized:
AI Agent Development and Deployment Framework
Foundation AI Models
- This component provides a wide range of foundation AI models, including machine learning, computer vision, and large language models.
- These models can be used immediately out-of-the-box or fine-tuned to match specific business needs, offering flexibility for various use cases and industries.
AI Agent Development Framework
- The framework offers a rich library of pre-built agents spanning multiple domains and industries.
- It exposes both no-code and low-code interfaces, allowing users to build agents and workflows easily without deep technical understanding.
- Additionally, it provides an Agent SDK for implementing custom agents based on specific business requirements.
Enterprise & Tool Integration
- This component focuses on seamless integration with existing enterprise applications, data sources and other 3rd parties through a comprehensive set of integration tools.
- It supports both native enterprise data connectors (SQL, NoSQL, Data Streaming, HTTPS) and modern open agent protocols (MCP, A2A), ensuring broad compatibility across different data sources and integration methods.
Agentic Orchestration & Management Engine
- The orchestration engine handles multi-agent workflow management with different paradigms, ranging from automation workflows to agentic decision making.
- Support for modern knowledge bases (RAG, GraphRAG), context-based awareness with continuous learning capabilities.
- Monitoring and observability for tracking agent performance and continuous enhancement.
- Security and governance services to ensure agents operate safely and in compliance with critical policies.
Cross-Platform Deployment
- This component utilizes container-based packaging to provide flexibility in deploying agents and workflows across multiple platforms, supporting both on-premise and cloud-based environments.
- This ensures scalability and adaptability to various infrastructure requirements.
Enterprise Agentic Framework
TMA has built a no-code AI framework for developing intelligent, agent-driven workflows with autonomous AI agents that understand context, make decisions, and act across systems.
client Knowledge Base
Leverage TMA Data Platform as the foundation for your enterprise's knowledge base, ingesting and structuring data from diverse sources.
Support variety of data types and sources
AI-Powered document parser
Incremental crawling & monitoring
Multi-Agent Orchestration
Orchestrate multiple AI agents to execute business workflows, combining rule-based automation with autonomous reasoning.
Hybrid intelligence framework
Context-aware execution
Integration
Open integration capability with Enterprise systems,enabling connectivity across existing IT infrastructure and third-party platforms.
Standardized agent connectors & API
No-code connector hub
Sample Solutions
Testing Agent
Patient Intake Assistant
client Service Agent
client Order Assistant
Description
This Agent can create test cases from user stories, simulate manual testing steps, and write automation scripts. This Agent can adapt to system changes with self-healing capabilities, reducing the need for constant test maintenance.
Functions
- Generate test cases from story: Automatically create test cases from user stories
- Mimic manual testing execution: Perform clicks, data entry, and navigation like a real tester
- Simulate automation scripting process: Write test scripts without manual coding
- Self-healing/maintenance: Adapt to UI changes automatically
Technologies
- Front-end: React, Next.js, TailwindCSS
- Back-end: Spring Boot, FastAPI
- Infrastructure: MinIO, PostgreSQL, Redis, Kafka, Keycloak, Kubernetes
- AI/LLMs: LangChain, LlamaIndex, Claude, OpenAI
Description
This solution simplifies patient check-ins and provides clear guidance. It also integrates with HIS, EMR, and scheduling systems for better coordination.
Functions
- Symptom input via text or image: Patients can submit symptoms by typing or uploading photos
- Medical AI understanding: Analyze inputs using trusted medical datasets for accurate assessment
- Clear health guidance: Deliver easy-to-follow advice based on symptom analysis
- System integration: Connect with HIS, EMR, and scheduling systems for updates and bookings
Technologies
- TMA Data Platform
- TMA Agentic AI Framework
- Gemini
Description
An AI assistant that responds to client questions in real-time. It learns from your documents, is easy to set up with a drag-and-drop interface, and works seamlessly across your website and social media channels
Functions
- Ingest knowledge from multiple data sources (web, text, doc, PDF, internal systems)
- Route complex cases to the right human agents with full conversation history and client data
- Track client satisfaction and emotional tone for continuous improvement
- Analyze queries and preferences to suggest relevant products or services in real time
- Provide instant translation for multilingual, global client support
- Integrate across websites and social media channels (Facebook, Telegram, TikTok, Zalo, etc.)
Technologies
- Front-end: Next.js, CopilotKit, Tailwind CSS
- Back-end: FastAPI, LangGraph, PostgreSQL, LightRAG
- Infrastructure: Docker
- LLM models: Qwen3, OpenAI
Description
An AI agent chatbot integrated across web and social channels to automate product queries, confirm purchases, and track orders - enhancing the full client journey.
Functions
- Sales assistant: Help clients find products, order, and get answers
- Auto order creation: Create orders instantly via system integration
- Auto handoff: Switch to a human agent when needed
- Easy integration: Integrate to websites and social platforms
Technologies
- Multi-agent framework: ADK, A2A, MCP
- AI & LLM models: OpenAI, Google AI
- Embedding models: OpenAI embeddings model, Google AI embedding model
- Backend layer: Python, FastAPI
- Database & storage: MongoDB, MinIO
