Building an Agent Configuration UI: Admin Panels for Non-Technical Users
Design and build admin panels that let non-technical users configure AI agent behavior through intuitive forms, real-time preview, validation feedback, and approval workflows.
Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Design and build admin panels that let non-technical users configure AI agent behavior through intuitive forms, real-time preview, validation feedback, and approval workflows.
Learn how to design a comprehensive evaluation framework for AI agents covering metric selection, dataset creation, and automated scoring pipelines that scale across dozens of agent capabilities.
Learn how to build a real-time orchestration dashboard for AI agent workflows. Covers UI components, status tracking, timeline views, error drill-down, and the backend API that powers it all.
Design a plugin system that lets third-party developers extend your AI agent's capabilities with custom tools, data sources, and integrations. Learn plugin API design, registration, sandboxing, and versioning patterns.
Learn how to create a plugin system for OpenAI Agents SDK that supports dynamic tool loading, hot-reloading during development, and isolated execution for third-party extensions.
Design and build developer-friendly SDKs for an AI agent platform, covering API client generation, error handling patterns, streaming support, and versioning strategies that maintain backward compatibility.
Design an agent template system that gives users pre-configured starting points for common use cases like customer support, data analysis, and content generation. Learn template architecture, customization points, and deployment pipelines.
Deploy AI agents on edge devices using Google's Gemma and Microsoft's Phi small language models. Cover resource requirements, agent patterns for constrained environments, and mobile deployment strategies.
Learn when and how to build agents using direct LLM API calls instead of frameworks, with a minimal implementation that demonstrates the agent loop, tool calling, and state management from scratch.
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