Results for “iterators”

Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.

Create new skills, modify existing ones, and measure their performance through iterative evaluation and benchmarking.

Progressively refines context retrieval in multi-agent workflows to solve the subagent context problem.

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture for rapid experimentation and synthesis, producing papers and presentations.

Discovers, audits, repairs, adapts, and designs bounded AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs.