Enterprise AI Agent Optimization
ROCQSMITH: Can Automatic Optimization Forge Better Proof Agents?
This analysis evaluates the efficacy of automatic AI agent optimization methods in formal verification, specifically for Rocq proof generation. We examine how different optimizers enhance proof-generation agents and assess the automation potential for fine-grained tuning components like prompt design, contextual knowledge, and control strategies.
Key Findings at a Glance
The study reveals significant performance gains from various optimizers, but also highlights a persistent gap compared to human-engineered solutions.
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Highest Optimized Agent Success Rate
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Human-Engineered SOTA Success Rate
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Baseline ReAct Agent Success Rate
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Max Performance Gain Over Baseline
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Few-Shot Optimization
Advanced Prompt Tuning
Contextual Knowledge Mgmt.
Control Flow Optimization
Leveraging Few-Shot Bootstrapping
BootstrapFewShot constructs few-shot prompts by collecting successful execution traces on a training set and directly including these demonstrations. This method proved to be the most consistently effective, particularly for ReAct-style agents. It significantly improved overall success rates from 19% to 40% in the DSPy framework, demonstrating its robust potential for foundational performance gains with minimal engineering.
While effective, the utility of few-shot demonstrations can be limited by context window constraints, especially when dealing with longer traces.
Exploring Advanced Prompt & Instruction Tuning
More sophisticated prompt-centric optimizers like MIPROv2, SIMBA, and GEPA were evaluated. MIPROv2, which jointly optimizes instructions and few-shot demonstrations, and SIMBA, which uses random search for prompt variants, achieved improvements in DSPy comparable to BootstrapFewShot, reaching 43% success rates.
However, these advanced methods did not consistently outperform simpler baselines for single-prompt agents, and GEPA, an evolutionary optimization strategy, actually underperformed the baseline in some settings (17% vs 19% for DSPy ReAct baseline), indicating their complexity doesn't always translate to superior results in this domain.
Challenges in Contextual Knowledge Management
Context-centric approaches like ACE and ReasoningBank aim to leverage past successful and failed executions by storing reflections in a structured knowledge base. ReasoningBank, which uses similarity-based retrieval to inject a small number of relevant items, showed some benefits.
In contrast, ACE, which injects up to 200 memories into the context, often led to degraded performance. This highlights a critical challenge: uncurated or weakly relevant context can introduce noise and harm model performance. Effective use of such systems requires careful consideration of agent inputs and potential pipeline adaptations.
Evaluating Control Flow & Topology Optimization
ADAS is designed to optimize an agent's control flow by generating executable agent code that defines the agent's overall decision logic. In our evaluation, ADAS performed inconsistently. Despite multiple optimization iterations, it did not show monotonic improvement, and the best-performing agents often emerged early in the process.
A key limitation observed was that ADAS-optimized agents exhibited strong bias towards the training data and relied on brittle, hard-coded decision logic, severely limiting their generality and robustness in new scenarios.
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Maximum Performance Gain for Optimized Agents Over Baseline ReAct
Enterprise AI Agent Workflow in Rocq
Observe Proof State
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Intermediate Reasoning (ReAct)
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Select Tactic to Apply
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Execute Tactic
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Inspect New Subgoals
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Proof Complete
| Optimizer | Agent Type / Impl | Max Total Success Rate | Key Insight |
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| BootstrapFewShot | ReAct (DSPy) | 40% |
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| MIPROv2 / SIMBA | ReAct (DSPy) | 43% |
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| ReasoningBank (Context-centric) | ReAct (Koog) | 25% |
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| ADAS (Control Flow) | Custom (Koog) | 17% |
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| Human-Engineered SOTA | RocqStar (Koog) | 53% |
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The Persistent Gap: Automated Agents vs. Human Expertise
Despite the measurable improvements demonstrated by various automatic optimization methods, a significant gap remains between the best-performing optimized agents (max 43% success rate) and carefully human-engineered state-of-the-art solutions like RocqStar (53% success rate). This indicates that while automation can reduce manual tuning effort, fully automated optimization of agent architectures for formal verification remains an open challenge.
The complexity of formal domains, the need for structured reasoning, and the nuanced interaction required with theorem provers still benefit substantially from human expertise in prompt engineering, tool orchestration, and contextual knowledge curation.
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Your Path to Optimized AI Agents
A structured approach to integrating and optimizing AI agents for formal verification and beyond.
Phase 1: Discovery & Assessment
Comprehensive analysis of existing proof generation workflows, identification of pain points, and evaluation of current agent performance baselines.
Phase 2: Strategy & Pilot Optimization
Develop tailored optimization strategies (e.g., few-shot bootstrapping, prompt tuning), implement a pilot Rocq proof agent, and measure initial performance gains.
Phase 3: Iterative Refinement & Expansion
Continuously refine agent prompts, contextual knowledge, and control mechanisms. Expand to additional verification tasks and integrate with your existing development pipeline.
Phase 4: Monitoring & Sustained Improvement
Establish robust monitoring for agent performance, feedback loops for continuous learning, and ongoing optimization to maintain high-performing, reliable proof agents.
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