Optimize multi-agent collaboration by learning task-specific interaction topologies. Use an LLM orchestrator to generate layered DAG topologies that adapt to inferred problem difficulty, treating agent interactions as a learned graph structure rather than fixed patterns.
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Optimize multi-agent collaboration by learning task-specific interaction topologies. Use an LLM orchestrator to generate layered DAG topologies that adapt to inferred problem difficulty, treating agent interactions as a learned graph structure rather than fixed patterns.
AgentConductor: Learned Topology Evolution for Multi-Agent Code Generation
Fixed agent collaboration patterns (e.g., always chain A→B→C) waste capacity on simple problems and under-scale complex ones. AgentConductor introduces a learnable orchestrator that dynamically generates task-adapted interaction topologies as directed acyclic graphs (DAGs). For each problem, the system infers difficulty, then constructs a topology balancing agent density with problem complexity—using sparse graphs for simple tasks, denser networks for complex ones.
The core innovation treats multi-agent interaction patterns as learned structures optimized via reinforcement learning. An LLM orchestrator observes problem specifications and task difficulty, then generates topology specifications in YAML format that deterministically describe how agents should interact.
Core Concept
AgentConductor operates through three stages:
Topology Specification: Represent multi-agent interaction as a layered DAG with agents as nodes and information flow as edges
Difficulty Inference: Estimate task complexity from problem statement using heuristics or learned models
Difficulty-Aware Generation: Use orchestrator to generate topology density proportional to inferred difficulty
Adaptive Refinement: During execution, evolve topology across multiple turns based on intermediate results
Architecture Overview
Input: Problem statement (code generation task, requirements, constraints)
Difficulty Estimator: Analyze problem to infer complexity (early/late refinement decision)
Orchestrator LLM: Generate topology specification based on difficulty signal
Topology Parser: Convert YAML to executable agent DAG
Multi-Turn Refinement: Iteratively update topology based on execution feedback
Output: Final code solution with traced agent interactions
Implementation Steps
Step 1: Design topology specification format
Create a structured representation for multi-agent interaction patterns.
Measure performance improvement relative to problem difficulty.
defevaluate_topology_performance(executor, test_problems):
"""
Benchmark: solve rate vs. problem difficulty.
Verify that difficult problems benefit most from denser topologies.
"""
results = []
for problem_spec in test_problems:
difficulty = estimate_problem_difficulty(problem_spec)
density = topology_density_from_difficulty(difficulty)
# Generate and execute topology
topology_yaml = generate_topology_from_orchestrator(
orchestrator, problem_spec, difficulty, density
)
topology_spec = TopologySpec(topology_yaml)
executor.topology = topology_spec
solution = executor.execute(problem_spec)
# Evaluate solution correctness
is_correct = check_solution_correctness(solution['debugger'], problem_spec)
results.append({
'problem': problem_spec[:100],
'difficulty': difficulty,
'density': density,
'correct': is_correct
})
# Aggregate by difficulty binsprint("Performance by Difficulty:")
for difficulty_bin in [0.25, 0.5, 0.75]:
bin_results = [r for r in results
ifabs(r['difficulty'] - difficulty_bin) < 0.1]
solve_rate = np.mean([r['correct'] for r in bin_results])
avg_density = np.mean([r['density'] for r in bin_results])
print(f" Difficulty {difficulty_bin:.2f}: {solve_rate*100:.0f}% solve rate, "f"avg density {avg_density:.2f}")
Practical Guidance
Hyperparameter Selection:
Difficulty estimation weights: Adjust [0.25, 0.35, 0.25, 0.15] based on domain (e.g., increase constraint weight for logic problems)
Density scaling: Linear, quadratic, or sigmoid—test which best aligns topology to performance
Max refinement iterations: 1-5. More iterations enable better adaptation; diminishing returns beyond 3.
Agent capability levels: Use 2-3 levels (basic, standard, advanced); more levels inflate agent count
When to Use:
Multi-agent systems with variable problem complexity
Code generation, planning, or complex reasoning tasks
Scenarios where different problems benefit from different collaboration patterns
Settings where inference cost can be amortized across multi-turn reasoning
