pattern-model-context-protocol

Interleave human feedback, approval gates, and clarification interrupts into autonomous agent loops for high-stakes decision points.

Orchestrate teams of specialized autonomous agents via hierarchical supervisor models, peer-to-peer debate, or pipeline handoffs.

Execute independent subtasks concurrently using Section Fan-Out/Gather or Voting Ensembles to reduce wall-clock latency and boost consistency.

Structure agent behavior into explicit planning phases: static plan-and-solve, dynamic ReAct loops, or hierarchical DAG decomposition.

Enable agents to adapt system prompts, heuristics, and tool parameters dynamically based on environmental feedback and error signals.

Continuously assess agent quality and detect behavioral regressions using LLM-as-a-Judge rubrics, pairwise evaluation, and golden datasets.

Architect resilient agents with multi-tiered fallback cascades, automatic parameter repair, exponential backoff, and circuit breakers.

Implement autonomous scientific inquiry and research loops: hypothesis formulation, experiment design, simulation, and iterative synthesis.

Track long-running agent progress toward quantifiable goals with milestone checkpoints, status monitors, and early stopping triggers.

Establish multi-layered defensive shields: input sanitization, prompt injection detection, PII masking, schema gates, and egress filters.

Establish standardized asynchronous communication protocols, Agent Cards discovery, and JSON-RPC message contracts across distributed agents.

Augment model context with external factual retrieval through query reformulation, hybrid dense-sparse indexing, re-ranking, and citation grounding.