Agentic AI

Superpowers is a methodology and framework designed to structure interactions with coding agents like Claude Code and Cursor. It uses composable skills to force agents to pause and clarify user intent before generating code. The system extracts specifications from conversation chunks to ensure alignment between the developer's goals and the agent's output.

• Provides a structured methodology to improve coding agent reliability and output quality.
• Supports major tools including Claude Code, Cursor, GitHub Copilot CLI, and Gemini CLI.
• Enforces a spec-first approach by asking clarifying questions before code generation begins.
• Uses composable skills to make agent behavior predictable and reusable across projects.

AgentGUI is a locally hosted graphical interface designed to help humans observe and control autonomous AI agents during complex, multi-session tasks. It provides rich visualizations of agent trajectories and supports both manual and automated steering mechanisms. The tool integrates with various open-source and frontier agent frameworks to coordinate concurrent sessions. A controlled user study showed that the interface significantly reduces the time needed to identify key elements in agent traces.

• AgentGUI offers a local GUI for real-time observation and control of long-running autonomous tasks.
• Visualizations and steering tools help bridge the gap between AI autonomy and human oversight.
• Integrates with multiple agent frameworks to coordinate concurrent sessions effectively.
• User study confirms a 38% reduction in time to identify key trace elements compared to baseline.
• Addresses the lag in human-centered interfacing as AI agent capabilities expand rapidly.

This research evaluates objective misalignment in LLM-powered multi-agent systems operating under asymmetric information and strategic deception. The authors use a modified Werewolf game to test agents with conflicting objectives across four model families and roles. The study analyzes both internal reasoning and public cheap-talk behavior to identify how hidden goals affect collective performance.

• Tests LLM deception in mixed-motive environments using a modified Werewolf game framework.
• Analyzes both internal reasoning and public cheap-talk across four model families.
• Reveals how asymmetric information drives strategic misalignment in agent objectives.
• Highlights risks of hidden goals in collaborative multi-agent deployments.

This paper introduces evidence-ledger adjudication to address the speed gap between AI-generated claims and human verification. The workflow pairs each claim with an evidence packet, assigns a support relation, and routes unsupported or contradicted items back to the author. Evaluated on a 2,335-row blind benchmark derived from AVeriTeC, CLIMATE-FEVER, and SciFact, the method hides gold labels during prediction to ensure unbiased scoring.

• Proves 0.676 relation accuracy vs 0.383 for non-agentic baselines on blind benchmarks.
• Automates routing of unsupported or mixed-evidence claims back to human authors.
• Uses external labels from AVeriTeC, CLIMATE-FEVER, and SciFact for rigorous validation.
• Demonstrates significant macro-F1 gains (0.601 vs 0.303) for traceability workflows.

Research shows that binary human-vs-bot detectors fail to distinguish AI agents browsing via automation, misclassifying roughly 35-39% of agent sessions as human. The study introduces a three-class framework that explicitly separates humans, bots, and AI agents. This architectural shift eliminates the confusion inherent in binary label spaces, achieving perfect per-class F1 scores on controlled benchmarks.

• Binary classifiers structurally cannot represent AI agents as a distinct traffic class.
• Adding an explicit agent class resolves misrouting of agent sessions to human labels.
• Three-class models achieved F1=1.000 across 30 runs in the study.
• Current bot detectors are insufficient for modern AI agent traffic patterns.
• Detection systems must evolve beyond simple human-vs-bot dichotomies.