Agentic AI for Simplified Data Visualization and API Access

The old development model required building a new API for every new data visualization or feature request. This was time-consuming and didn't scale. The team wanted a way to make their data more dynamically accessible without continuously expanding their API surface area.

Rather than building a dedicated API endpoint for every use case, the team defines tools that wrap existing APIs and lets the agent decide which to call. System prompts describe each tool's purpose and usage. This shifts development effort from building new endpoints to building tools and refining agent prompts.

Estimated 50–80% reduction in development time for new features. Development effort is now concentrated on the agent platform and tool definitions rather than on building and maintaining individual endpoints.

Each tool has its own system prompt describing its API, inputs, and outputs. Response message construction is also handled through the agent framework. The team is working toward better context and memory management to reduce hallucinations. CI/CD runs on GitHub Actions with Terraform for infrastructure deployment.

Response latency — agent tool calls that chain multiple API requests can be slow. Hallucination is attributed to insufficient internal memory and context management. Pricing is currently manageable but expected to rise significantly as user volume grows. Tool choice was partly shaped by AWS sponsorship — others should evaluate tools more independently.