Builds an autonomous iterative research loop that narrows a wide uncertainty/exposure range toward a user-defined target by issuing Google searches, extracting evidence from results, re-estimating the range from accumulated evidence, and repeating until converged. Use when the user wants to implement autonomous research that progressively reduces uncertainty toward a quantified goal — risk analysis, market sizing, due diligence, literature review, or any domain where a wide estimate must be narrowed with real evidence.

Autonomous multi-source security intelligence agent. Scans GitHub PRs for vulnerabilities using cross-source context from Slack and codebase architecture. Finds risks that single-source scanners miss by correlating data across tools.

Autonomous product feedback monitoring agent. Ingests signals from GitHub, reviews, Slack. Learns what matters to you. Calls you when it's critical. Gets smarter with every interaction.

Autonomous incident response and self-healing codebase agent. Use when building SRE automation, incident pipelines, error detection, auto-remediation, or production monitoring systems. Covers the full lifecycle from error ingestion to diagnosis, fix generation, approval gating, phone escalation, and deployment.

Autonomous agent that turns customer signals (emails, calls) into engineering actions across Jira, Notion, and Slack — zero human intervention.

Guides agents through autonomous ManiSkill and VSLAM evaluation, tuning, verification, memory storage, and summary writing using the Autolab MCP server and repo tooling.

Build self-improving runtime security for autonomous AI agents — intercept actions, dispatch adversarial investigators, generate evolving scoring rules, and enforce deterministic block decisions with no LLM in the enforcement path.

Ambient stress detection agent that monitors developer fatigue via webcam and autonomously takes over GCP Cloud Shell or browser tasks when stress threshold is crossed. Powered by Gemini 3 Flash vision, Railtracks orchestration, and Augment Code codebase context.

Iteratively optimize thermal designs by solving 2D heat equations (Poisson PDE). Parameterize heat source placement and material conductivity, simulate temperature distributions, evaluate performance metrics, and propose improvements autonomously. Use when the user asks to design, optimize, or analyze heat sinks, thermal layouts, cooling systems, or any steady-state thermal problem on a 2D domain.