Overview

AI agents are software entities designed to perform tasks autonomously. They leverage local Large Language Models (LLMs) to understand and execute complex instructions. By hosting these agents locally, organizations can avoid ongoing API fees and subscriptions, achieving scalability and cost-efficiency. These AI agents can be employed in diverse areas such as customer support, data analysis, content generation, and more.

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A company engaged us to develop a Proof of Concept (POC) for a platform that could efficiently manage these AI agents and their workflows. The goal was to streamline task assignments, enhance visibility, and ensure timely execution without relying on expensive external APIs.

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Day 1-3: Problem Identification and Planning We started by engaging with key stakeholders to understand their needs.

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Our discussions revealed three major challenges:

• Inefficient task management: Manual task assignments led to frequent errors and delays.

• Lack of transparency: Monitoring agent progress was difficult, leading to missed deadlines.

• Delayed execution: The slow workflow hampered overall efficiency.

With these insights, we proposed a solution: a custom platform to manage AI agents and workflows efficiently.

Day 4-5: Architectural Design

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We designed the architecture with the following features:

1. Agent Management: Users can create and manage agents with specific roles and goals.

2. Task Assignment: Dynamic task assignment to agents.

3. Hierarchy Visualization: Clear visualization of task delegation and workflow.

4. Workflow Execution: Automated task execution with detailed results.

We included a rating system to evaluate agent performance, fostering a competitive spirit.

Day 6-7: Core Functionality Development

Our development phase focused on core functionalities:

• Agent and Task Management: Streamlit interfaces were created for adding and managing agents and tasks.

• Workflow Execution: Integrated OpenAI’s API to handle tasks and simulate real-life scenarios.

Flexibility was key. The system could adapt to changes, such as adding or removing agents and reassigning tasks.

Day 8-9: Enhancing User Experience

We improved the user interface:

• Logo Integration: Added logos to the main interface and sidebar for a professional look.

• Sidebar Controls: Easy-to-use controls for creating agents and tasks.

• Hierarchy Visualization: Used Graphviz for visual clarity.

• Performance Ratings: Included a slider for rating agent performance.

Day 10: Testing and Feedback

Testing was rigorous. We simulated various scenarios:

• Multiple agents managing tasks simultaneously.

• Dynamic task reassignment.

• Handling workflow interruptions and resumptions.

Feedback from the client’s team helped refine the system. We fixed usability issues and ensured robustness.

Day 11: Final Adjustments and Documentation

Final adjustments were made based on feedback. We also prepared documentation, including:

• A user manual detailing each feature.

• Troubleshooting tips.

• Best practices for optimal use.

Day 12: Presentation and Handover

We presented the POC to the client’s stakeholders:

• Live Demo: Showcased agent creation, task assignments, hierarchy visualization, and workflow execution.

• Impact Analysis: Demonstrated potential time savings and efficiency improvements.

• Future Roadmap: Suggested enhancements like advanced analytics and CRM integration.

The stakeholders were impressed with the results and the rapid development timeline. They appreciated the clear narrative from problem identification to solution delivery.

Conclusion

In just 12 days, we developed a custom AI agent workflow management platform that addressed the client’s challenges. This POC streamlined their processes and set the stage for future advancements.

By focusing on rapid prototyping and iterative development, we showed how agile methodologies and advanced technologies like Streamlit and OpenAI can deliver significant value swiftly.

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