Agentic AI

This paper argues that prompt-based guardrails are brittle because agents inheriting human credentials can bypass intent-based limits. The authors propose Out-of-Band Policy Enforcement (OBPE), which operates at a trusted boundary outside the agent's reasoning loop. OBPE authorizes typed operations, narrows queries before backend execution, and filters or masks response data based on semantic gating.

• Prompt instructions are insufficient for security; agents can override reasoning limits.
• OBPE enforces policy at the tool boundary, separate from the LLM.
• Pre-execution query narrowing reduces data exposure in model context.
• Semantic gating allows denial or holding of calls based on argument values.
• Response filtering masks sensitive fields before data returns to the agent.

OpenMAIC v1.0.0 introduces a Pro workbench alongside its one-click generator, allowing users to steer curriculum creation via a chat-first agent interface. The system plans, builds, and revises course materials directly from user inputs. Durable sessions ensure server-backed runs survive restarts, enabling users to cancel, resume, and adjust workflows at any time.

• Chat-first agent interface plans and revises entire course structures interactively.
• Durable server-backed sessions allow pausing, resuming, and steering of long-running tasks.
• One-click generation remains available alongside the new Pro workbench for quick starts.

Research into Qwen3-32B reveals that models equipped with tools still commit to final claims without sufficient evidence, despite explicit instructions against guessing. The study isolates two metrics: occurrence of unsupported claims and conditional repair rates when missing evidence is provided. In testing, 33 out of 512 initial responses contained unsupported established claims before any repair attempts.

• Models ignore available tools to resolve uncertainty, violating explicit no-assumption instructions.
• Occurrence metric measures unsupported claims using only visible evidence, not the ground truth.
• Conditional repair assesses how often models fix claims when the missing evidence is supplied.
• Qwen3-32B showed significant failure rates on new prompt templates despite tool access.
• This highlights a gap between tool availability and the model's willingness to query them.

WM-R1 introduces a reinforcement learning framework for mobile GUI agents that replaces real Android environment interactions with world models during training. This approach eliminates the high resource costs and instability associated with extensive real-world rollouts. The system embeds these world models into the agent's reasoning process, allowing it to predict action consequences before execution.

• Replaces real Android environments with world models for all training rollouts.
• Reduces resource costs and instability inherent in real-environment RL.
• Enables agents to simulate action consequences before committing to them.
• First RL framework to integrate world models directly into GUI agent reasoning.

Credo addresses the opacity of imperative agent harnesses by extracting reusable declarative primitives from searched programs. It captures logical steps, runtime signals, execution decisions, and prompt strategies to enable cross-task reuse. This approach allows subsequent tasks to leverage prior knowledge instead of restarting the search process from scratch.

• Moves beyond opaque imperative code to explicit, reusable workflow primitives.
• Extracts logical steps and critical runtime signals from successful agent runs.
• Enables cross-task reuse of execution and prompt strategies.
• Reduces redundant search overhead for new agentic tasks.

This GitHub repository aggregates a collection of Model Context Protocol (MCP) servers, serving as a discovery resource for developers. It links to an external catalog at glama.ai for browsing available implementations. The repo acts as a centralized directory rather than a standalone tool.

• Use as a discovery hub to find existing MCP server implementations
• Links to glama.ai for broader server catalog browsing
• Helps identify community-driven tools for LLM context integration
• No direct code downloads; serves as a curated index
• Useful for evaluating available MCP options before building custom servers