In the symphony of innovation, TRIZ has long served as the structured score guiding engineers toward inventive breakthroughs. But what happens when you give the orchestra to a team of AI agents? Enter TRIZ Agents, a bold exploration of how large language model (LLM) agents—armed with tools, prompts, and persona-based roles—can orchestrate a complete innovation cycle using the TRIZ methodology.
Cracking the Code of Creativity
TRIZ (Theory of Inventive Problem Solving), derived from the study of thousands of patents, offers a time-tested approach to resolving contradictions in engineering design. It formalizes the innovation process through tools like the 40 Inventive Principles and the Contradiction Matrix. However, its structured elegance demands deep domain expertise—something often scarce outside elite R&D centers.
The rise of agentic AI systems—popularized by frameworks like AutoGen, CAMEL, and ChatDev—has triggered a shift from solo LLMs to ensembles of specialized agents collaborating on tasks. This mirrors the broader trend in AI where scale and generality are now complemented by modular specialization and cooperative behavior. TRIZ Agents rides this wave by embedding LLMs within a structure of cognitive scaffolding, domain separation, and procedural discipline.
Meet the TRIZ Agents
Using the LangGraph framework, the authors construct a multi-agent LLM architecture where each agent—Mechanical Engineer, Safety Engineer, TRIZ Specialist, and more—has its own persona, role, and access to specific tools. Overseeing the process is a Project Manager agent, orchestrating each step of the TRIZ process like a conductor.
Agents communicate using natural language, query tools like a TRIZ parameter list or contradiction matrix, and document progress at each step. The entire system is inspired by how human teams work: one step at a time, referencing past documentation, but not retaining memory between sessions.
Case Study: Lifting Innovation with Gantry Cranes
To evaluate the framework, the authors simulate a TRIZ-based innovation session focused on improving gantry crane systems. The challenge: minimize hazardous swing and overheating when lifting heavy loads quickly. The AI team broke the problem into six structured steps:
- System Definition — The Mechanical Engineer identified key components (trolley, wire rope, hoist) and their supersystems (environmental conditions, workers).
- Functional Analysis — Each component’s function was mapped, with special attention to interactions (e.g., motor driving hoist, hoist lifting load). While the system found most elements, it missed some safety-related human interactions.
- Cause-Effect Chain Analysis (CECA) — Overloading and excessive speed were highlighted as root causes. The agents identified five additional, plausible causes, showing generative depth beyond the reference case study.
- Engineering Contradictions — The TRIZ Specialist used contradiction matrices to identify issues like “Speed vs. Stability” and “Load Capacity vs. Safety”—close analogs to the human-designed contradictions.
- Physical Contradictions — Agents correctly framed dual needs like “move fast vs. move slow” and “lift heavy vs. ensure safety.”
- Ideation and Solutions — The agents proposed applying Sliding Mode Control with anti-sway trajectory—a near match with the human study. They also suggested thermal enhancements, but missed the idea of intelligent circuit breakers, possibly due to the Electrical Engineer agent being left out.
These findings show that, even without memory or feedback loops, the agent team produced structured and plausible design solutions.
Lessons in Orchestration
The study emphasizes that agent prompts act like cognitive scaffolding: change the prompt, and the agent’s behavior—and results—change drastically. Prompt engineering here isn’t just nudging—it’s choreography. The system used a supervised team model, with a Project Manager distributing tasks. However, it lacked a feedback loop—no agent was ever asked to revise or challenge previous results. That’s like an orchestra playing once through a new piece without rehearsals.
More provocatively, the underutilization of the TRIZ RAG tool by the TRIZ Specialist agent highlights a core problem in current multi-agent design: autonomy vs. compliance. If you let LLMs think for themselves, they might skip the homework.
Implications: When LLMs Join the Innovation Department
What TRIZ Agents proves is not just that LLMs can follow structured creativity frameworks—it shows that multi-agent orchestration enables emergent problem-solving behaviors. These agents aren’t just role-playing; they’re collectively thinking. The implication is clear: in a near-future R&D department, your brainstorming team might include a Control Systems Agent, a TRIZ Agent, and yes, a Documentation Agent—all synthetic, all tireless, all iterative.
As AI tools become increasingly integrated with domain-specific logic and toolkits, expect methodologies like TRIZ, Lean Innovation, and Design Thinking to become computational playbooks. Systems like TRIZ Agents could serve as always-on co-pilots for innovation workflows across mechanical design, software architecture, policy design, or even creative writing.
With orchestration models growing more sophisticated, we may soon see autonomous idea refinement, long-term memory, and inter-agent critique cycles. Agent swarms that argue, iterate, and evolve their ideas—essentially, corporate think tanks on demand.
Cognaptus: Automate the Present, Incubate the Future.