Conversational assistants powered by large language models (LLMs) excel at tool-use tasks but struggle with adhering to complex, business-specific rules. While models can reason over business rules provided in context, including all policies for every query introduces high latency and wastes compute. Furthermore, these lengthy prompts lead to long contexts, harming overall performance due to the ’needle-in-a-haystack’ problem. To address these challenges, we propose a multi-stage alignment method that teaches models to recall and apply relevant business policies during chain-of-thought reasoning at inference time, without including the full business policy in-context. Furthermore, we introduce a novel Policy Recall reward based on the Jaccard score and a Hallucination Penalty for GRPO training. Altogether, our best model outperforms the baseline by **16 points** and surpasses comparable *in-context* baselines of similar model size by **3 points**, while using **40% fewer words**.

The rise of fully autonomous agents necessitates a rigorous evaluation of their reliability under **real-world API complexity**. Accordingly, moving beyond perfectly **sanitized** settings that disregard realistic obstacles, we introduce ***WildAGTEval***, a configurable benchmark environment that accounts for two critical dimensions of realistic API complexity: **API specification**, which includes detailed documentation and usage constraints, and **API execution**, which captures runtime challenges. Consequently, ***WildAGTEval*** offers (i) an API system spanning 86 functions across 7 domains with 60 independently activatable complexity scenarios, yielding approximately 32K distinct test configurations, and (ii) 300 multi-turn conversations grounded in this system. Through systematic evaluation of 11 state-of-the-art LLMs, we find that: (1) individual complexities reduce accuracy by up to 23.1%; (2) compounding them amplifies this drop to 52.1%; and crucially, (3) failing agents fabricate success and distort intent rather than admitting limitations, exposing a critical reliability gap invisible to existing benchmarks.

As Large Language Model (LLM) agents increasingly leverage the Model Context Protocol (MCP) to operate in complex environments, the expansion of their action spaces offers agents unsafe capabilities and underscores the risk of power-seeking. While broad action space and greater environment influence are essential for task fulfillment, they creates a fragile risk surface where minor errors or hallucinations are magnified into catastrophic failures. In response, we propose SafeMCP, a server-side defense plugin that constrains tool acquisition via predictive reasoning regarding future safety risks. SafeMCP utilizes an internal world model for look-ahead reasoning to implement a two-tier defense: proactive tool filtering to constrain hazardous power expansion and immediate intervention as a fail-safe. To train SafeMCP, we introduce a three-stage pipeline comprising environmental dynamic grounding, safe policy initialization, and reinforcement learning (RL) with dual verifiable rewards. Experiments on PowerSeeking Bench, ToolEmu, and AgentHarm show that SafeMCP achieves a safe equilibrium, effectively mitigating risks while preserving agent utility.