AI Agent Developer With LangGraph and CrewAI
Starting at
$
40
/hrAbout this service
Summary
FAQs
What kind of problems can an AI agent solve for my business?
AI agents are designed to automate complex workflows, handle repetitive tasks, and provide intelligent insights. For example, an agent can manage customer support inquiries, automate data entry and analysis, or even assist in content creation and scheduling. By understanding your specific pain points, I can design a custom agent that integrates seamlessly into your operations to improve efficiency and productivity.
What is your development process like, and how involved do I need to be?
My process is collaborative and transparent, starting with an in-depth discovery phase to fully understand your goals. We'll then move to strategy and design, followed by agile development sprints where you'll receive regular updates. Your involvement is key during the initial stages and for providing feedback during development to ensure the final product aligns perfectly with your vision.
Why do you use frameworks like LangGraph and CrewAI?
I use LangGraph for its powerful ability to create complex, stateful, and reliable AI workflows, which is ideal for intricate tasks requiring precise control. For projects that require a team of specialized AI agents to collaborate on a task, CrewAI provides a more intuitive and rapid development environment. The choice of framework depends on the specific needs of your project, ensuring the most efficient and effective solution.
How do you ensure the AI agent is reliable and performs well after deployment?
Every AI agent is built for scale and reliability from the ground up. I use Docker for containerization and deploy on robust cloud platforms like AWS or GCP, which allows for scalability. Post-deployment, I set up comprehensive monitoring using tools like Langsmith to track performance, identify potential issues, and ensure the agent operates at its peak.
What does the post-launch support look like?
My service doesn't end at deployment. I provide a dedicated period of post-launch support to address any immediate questions or issues that may arise. Additionally, I offer a knowledge transfer session to walk your team through the agent's architecture and a maintenance guide for its ongoing operation.
How many changes can I request?
My process is built on collaboration and includes dedicated feedback stages. We'll have a key review and revision cycle after the initial design phase, and smaller feedback loops at the end of each development sprint. This ensures your vision is perfectly captured at every critical step of the project.
What's included
Custom AI Agent Strategy & Design
Before a single line of code is written, a comprehensive strategy and design document will be delivered. This foundational document ensures alignment with the client's vision and goals. - In-depth Consultation: A thorough discussion to understand the client's specific problems and objectives. - Workflow Analysis: A detailed mapping of the proposed AI agent's workflow and its integration with existing systems. - Technology Stack Proposal: A clear recommendation of the optimal technologies, including frameworks like LangGraph and CrewAI, tailored to the project's needs. - Success Metrics: Defined key performance indicators (KPIs) to measure the agent's effectiveness post-deployment.
Production-Ready AI Agent
This is the core deliverable: a fully functional and intelligent AI agent engineered to solve the client's specific challenges. - Custom-Built Intelligence: An AI agent developed using LangGraph for complex, stateful applications or CrewAI for collaborative, multi-agent workflows. - Scalable Architecture: The agent will be built for scalability and reliability, ensuring it can handle growing demands. - Thoroughly Tested Code: The agent will undergo rigorous testing to ensure it is robust and free of critical bugs.
Seamless Cloud Deployment & Integration
A powerful AI agent is only effective if it's properly deployed and integrated. This deliverable guarantees a smooth transition to a live environment. - Containerization: The application will be containerized using Docker for portability and consistency across different environments. - Cloud-Native Deployment: Deployment to the client's preferred cloud platform (AWS or GCP) using modern infrastructure-as-code practices. - CI/CD Pipeline: A continuous integration and continuous delivery pipeline for automated testing and deployment of future updates.
Comprehensive API & Documentation
To ensure the client can easily interact with and understand the AI agent, this deliverable provides a well-documented API. - High-Performance API: A FastAPI-powered API for efficient and asynchronous communication with the AI agent. - Interactive API Documentation: Automatically generated, interactive API documentation (like Swagger UI) for easy testing and integration. - Clear Usage Guides: Detailed documentation explaining how to use the API endpoints and interact with the agent.
Robust Monitoring & Observability Setup
Post-deployment, it's crucial to monitor the AI agent's performance and health. This deliverable provides the necessary tools and dashboards. - Performance Monitoring: Integration with monitoring platforms like Langsmith or other observability tools to track the agent's performance and resource usage. - Custom Dashboards: Creation of customized dashboards to visualize key metrics and the overall health of the agent. - Alerting System: Configuration of alerts to notify the client of any critical issues or performance degradation.
Knowledge Transfer & Support
Empowering the client to understand and manage their new AI agent is a key part of the service. - Code Walkthrough Session: A dedicated session to walk the client's technical team through the codebase and architecture. - Maintenance Guide: A document outlining best practices for maintaining and updating the AI agent. - Post-Launch Support: A defined period of post-launch support to address any immediate questions or issues.
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