agent-long-context-benchmark

name	agent-long-context-benchmark
title	AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.20730
keywords	["agent-benchmark","long-context","evaluation-framework","environment-simulation","task-generation"]
description	Build controllable benchmarks for evaluating long-context agents using environment rollouts. Generate diverse multi-step agent tasks that require maintaining context across extended interaction sequences, enabling evaluation of agent reasoning quality in scenarios with long history requirements.

Problem

Existing agent benchmarks typically use short, isolated tasks that don't reflect real-world requirements where agents must maintain context and reason over long interaction histories. Long-context agents need benchmarks that can systematically vary task complexity, history length, and environmental conditions to properly evaluate their capabilities.

Solution

Create AgentLongBench: a controllable benchmark framework that generates synthetic long-context agent tasks through environment rollouts. This approach allows you to:

1. Control Task Complexity: Vary the number of steps, branching decisions, and tool interactions
2. Simulate Extended Histories: Generate long chains of observations and actions that agents must reason over
3. Test Context Window Usage: Evaluate how agents handle increasing history lengths
4. Generate Diverse Scenarios: Create varied environmental states and task requirements programmatically

When to Use

• Evaluating long-context language models on agent tasks
• Testing agent memory and reasoning capabilities with extended histories
• Benchmarking context window limitations and efficiency
• Developing agents for complex multi-step workflows (code generation, research, planning)
• Analyzing how agent performance degrades with longer interaction histories

When NOT to Use

• Short, single-turn interaction tasks (standard benchmarks suffice)
• Real-world task evaluation (use human-curated benchmarks)
• Tasks requiring specialized domain knowledge (this is synthetic evaluation)

Implementation

Step 1: Define the Environment Simulator

Create a controllable environment that generates long-context agent interactions.

class LongContextEnvironment:
    """Simulation environment for generating long interaction sequences"""

    def __init__(self, complexity_level=1, max_steps=50):
        self.complexity = complexity_level  # 1-5 scale
        self.max_steps = max_steps
        self.state = self.initialize_state()
        .step_count = 
        .history = []

     ():
        
         {
            : .generate_goal(.complexity),
            : .generate_objects(.complexity),
            : .available_actions(),
            : 
        }

     ():
        
        .step_count += 

        
        is_valid = .validate_action(agent_action)
          is_valid:
            observation = .get_observation()
             observation, -, , {: }

        
        result = .apply_action(agent_action)
        observation = .get_observation()

        
        .history.append({
            : .step_count,
            : agent_action,
            : result,
            : .state.copy()
        })

        
        progress_delta = .get_progress() - .state[]
        reward = progress_delta *   
        .state[] += progress_delta

        done = (.step_count >= .max_steps)  .goal_reached()

         observation, reward, done, {: (.history)}

     ():
        
         {
            : .step_count,
            : .state[],
            : .get_visible_objects(),
            : .history[-:],  
            : .state[],
            : .state[]
        }