#3453 Innovation with Multi-AI-Agent Applications

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#3453 Innovation with Multi-AI-Agent Applications

#3453 With a multi-AI-agent application, how could I best employ innovative thinking methods to identify opportunities for providing innovative solutsion, and suggesting innovative solutions? Consider the full gamut of innovative thinking methods

Below is a workflow blueprint you can adapt to any multi-AI-agent framework (LangChain Agents, CrewAI, Autogen, Haystack Orchestrator, etc.). It shows where in the pipeline each innovative-thinking method fits and which specialized agent performs it. Feel free to rename or merge agents as your stack demands.

1. Data-Ingestion & Opportunity Radar

AgentPurposeTypical AI ToolsKey Inputs
Horizon-ScannerCrawl patents, news, social feeds, R&D papers; tag weak signals & emerging trends.web APIs, semantic search, embeddingsGoogle Scholar, arXiv, USPTO, Crunchbase
Context-ProfilerBuild “jobs-to-be-done” problem statements from customer reviews, support tickets, forums.sentiment analysis, topic modelingApp Store reviews, Zendesk logs

Innovative methods leveraged: Foresight scanning (PESTLE, STEEP), Jobs-to-Be-Done (JTBD) interviews synthesized via LLM.

2. Opportunity Distillation

AgentPurposeInnovative Method(s)
Trend-ClustererGroup raw signals into opportunity themes via hierarchical clustering & keyword co-occurrence.Morphological analysis (dimensions × values), Mind-mapping
Constraint-MapperTranslate each theme into a set of contradictions or performance gaps.TRIZ contradiction matrix

3. Idea Generation Swarm

Spin up several parallel ideation agents—each embodies a different creative lens. They all receive the same structured “opportunity packet” and return candidate concepts.

Ideation AgentMethod it embodiesPrompting Strategy
SCAMPER AgentSubstitute, Combine, Adapt, Modify, Put to other uses, Eliminate, ReverseChain-of-Thought prompt template cycling through the 7 verbs
TRIZ Agent40 inventive principles, separation principlesMatch constraints → ask LLM to rank top 3 principles → generate concepts
SIT AgentSystematic Inventive Thinking (Function Follows Form)Identify five “templates” (Subtraction, Multiplication…) → force-fit
Blue-Ocean AgentValue-Innovation & Strategy CanvasCompare current value curves → suggest elimination/reduction/raise/create
Design-Thinking AgentEmpathy → Define → Ideate snapshotsUses persona & journey-map embeddings → pain-point-driven ideas
Lateral-Thinking AgentRandom stimulus, provocation (Po), reversalInject unrelated Wikipedia sentences or images → analogy prompts
Biomimicry/Analogies AgentNature-inspired & cross-domain analogiesvector-search biological solutions & landmark patents
AI-Combination AgentLarge Combinatorial SearchUses evolutionary algorithms over idea descriptors to breed hybrids

4. Idea Fusion & Refinement

AgentTasksMethod(s)
Concept-SynthesizerMerge overlapping ideas; remove duplicates; build multi-feature bundles.K-J Affinity, Morphological Box recombination
Feasibility-ScorerRapid techno-economic analysis, SWOT, TRL estimate, risk scoring.Weighted decision matrix, Monte-Carlo cost range
Impact-EstimatorSize market, carbon/ESG impact, strategic fit.Blue-Ocean “eliminate-raise-create-reduce” metrics, JTBD importance v. satisfaction gap

Outputs: ranked Concept Cards (title, 2-line_summary, key differentiator, feasibility, impact score).

5. Prototype & Experiment Loop

AgentFunctionMethod(s)
MVP-PlannerDecompose concept into riskiest assumptions; design rapid experiments or digital twins.Lean Canvas, Riskiest Assumption Testing
Simulation-AgentRun physics, economics, UX sims or low-code prototypes; capture metrics.Digital-twin modeling, A/B bandit testing
Learning-Loop CoordinatorCompare experiment results vs. hypothesis; decide pivot, iterate, or kill.Build-Measure-Learn (Lean Startup), Hypothesis-Driven Dev

6. Knowledge Base & Memory

A shared vector/RAG store keeps:

  • Opportunity packets & source docs

  • All generated concepts + lineage to method & agent

  • Experiment results / metrics

  • Final solution blueprints & marketing collateral

Schema (simplified):

Opportunity(id, theme, pains, JTBD, constraints, market_size)
Idea(id, opportunity_id, agent_origin, method, concept_text, feasibility, impact)
Experiment(id, idea_id, hypothesis, test_plan, result_json, decision)
Asset(id, idea_id, type [pitch|spec|ad_copy], file_link)

7. Orchestration Patterns

  1. Broadcast-Gather: Dispatcher sends the same opportunity to every ideation agent; gathers responses; passes to Synthesizer. Great for breadth.

  2. Tournament: Quick scoring after step 3; only top N concepts advance to deeper TRIZ/SIT elaboration—saves compute.

  3. Evolutionary League: Concepts become a population; Genetic-Programming Agent cross-breeds feature sets; fitness = composite score (impact × feasibility).

8. Implementation Tips

TipWhy it matters
Use role-prompting + tool-calling for each agent so methods stay distinct (e.g., “You are a TRIZ expert…”)Keeps reasoning traces clean for auditability
Store agent rationale (chain-of-thought) in the KB but summarize for humansEnables later explainability & improvement
Feedback weights from prototype metrics can fine-tune the scoring model, gradually biasing toward commercially viable ideasBuilds learning organization into the system
Leverage function-calling to run external calculators (cost, carbon) inside agent loopsPrevents hallucinated numbers

Putting It All Together

  1. Capture signals → Horizon-Scanner

  2. Frame constraints → Constraint-Mapper

  3. Parallel ideation → Seven+ creative agents

  4. Fuse & rank → Concept-Synthesizer + Scorers

  5. Prototype & validate → MVP-Planner loop

  6. Archive & serve insights → KB with RAG interface

Using this structure, you systematically apply the full gamut of innovative-thinking methods—each embodied by a purpose-built AI agent—while the orchestration layer turns individual sparks into a repeatable innovation pipeline.


From <https://chatgpt.com/>  ChatGPT o3

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