Development Working Group
Problem Statement: Organizations need AI agents to automate workflows, but face an impossible choice: expensive custom development for each use case or rigid pre-built solutions that don't adapt. Current agents remain static after deployment, unable to learn and adapt from experience or evolve with changing requirements.
Solution: This project develops a universal self-evolving agent architecture that adapts to any domain through configuration changes alone. Using strands-agents SDK and cloud infrastructure, the same codebase powers everything from simple customer service to complex software development by adjusting only prompts and tools. The system intelligently scales from single agents for basic tasks to coordinated teams for complex projects, while continuously learning from every deployment. Validation across five industries proves that specialized behaviors emerge from configuration, not custom code - delivering the promise of one codebase, infinite business applications.
Goals & Success:
[Primary Goal] - Showcase Production-Proven Patterns: Demonstrate reliable and adaptable agentic systems, sharing lessons from real-world deployments across various industries.
[Secondary Goal] - Build Self-Evolving Systems: Create a framework where agents can autonomously improve, optimize workflows, evolve tools, and spawn specialized sub-agents based on environmental feedback.
[Third Goal] - Drive Practical Innovation: Move beyond hype by delivering reproducible reference architectures, best practices that solve real business problems, and answering core questions in agentic design.
Working Group Information
Slack: #agentic-community-wg-development
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