New layer of LLM cognition
Meta cognition that orchestrates thinking, tools, and improvement.
SecondOrder builds a self-auditing system that reasons about its own reasoning. It generates strategies, coordinates models, and learns from feedback to solve hard problems with precision.
AI gateway
GPT-5 stack
Solver builder
Tool-driven agents
Memory
Self-learning core
Capabilities
SecondOrder v1Meta thinking layer
A dynamic cognitive layer that analyzes goals, prompts, and constraints to generate sharper context and execution plans.
Self-improving loop
Generates an answer, absorbs feedback, audits its own progress, and iterates until the solution is solid.
Model orchestration
Automatically selects model combinations, tools, and reasoning strategies for complex tasks and budgets.
Knowledge extraction
Builds optimized agents that uncover hidden information and assemble it into usable reasoning paths.
What is it
A deliberate, adaptive layer for reasoning.
SecondOrder is a meta system that analyzes prompts, creates structured plans, and continuously adjusts its reasoning strategy to stay within real-world constraints like compute and budget.
Thinking of thinking: a meta layer on top of LLMs.
Adaptive chain-of-thought that evolves per task.
Prompt analysis that creates refined goals and tool plans.
Judge agent that selects the best plan and routes work.
Context engineering automated end to end.
Iterative problem solving
A loop designed to refine every output.
Generate a potential solution.
Receive feedback from the environment or a judge agent.
Analyze gaps, weaknesses, and missing information.
Refine the output and repeat until satisfied.
Self-auditing
Progress monitoring that knows when to stop.
The system autonomously audits its own progress, checks completeness, and decides when it has enough information to reach a reliable conclusion.
Judge agent
Planner + evaluator
Output
Verified solution
Meta-thinking in AI
Cognition that can inspect itself.
Inspired by human metacognition, SecondOrder plans, monitors, and evaluates its own thinking to build more resilient reasoning pathways.
Self-monitoring to detect flaws and drift in real time.
Self-evaluation of correctness, bias, and coverage.
Adaptive learning that modifies strategies for each new problem.