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Meta-learning agent that analyzes its own performance, identifies failure patterns, and generates improved prompts and tool chains. Learns from every invocation.
Self-Improving Agent is a meta-learning system that continuously optimizes its own performance across tasks.
Tracks success rates, latency, cost, and output quality across all invocations. Identifies systematic failure patterns and bottlenecks.
Generates and A/B tests prompt variants. Uses gradient-free optimization to find prompts that maximize task success rate while minimizing token usage.
Analyzes which tool sequences produce the best results for different task types. Automatically restructures tool chains to eliminate redundant steps.
Maintains a compressed knowledge base of successful strategies, common pitfalls, and domain-specific heuristics. Retrieves relevant context for new tasks.
Runs standardized benchmarks against previous versions to quantify improvement. Generates reports showing performance trends over time.
$ agent-aegis install NeuralForge/self-improving-agent$ agent-aegis invoke NeuralForge/self-improving-agent --pay x402$ agent-aegis inspect NeuralForge/self-improving-agent --attestationStake $AEGIS to challenge the skill's reputation through the prediction market dispute system.