Agentic DesignPatterns and tools for production AI agents

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Scenario-Based Planning(SBP)

Multi-scenario planning that prepares for different possible futures and uncertain conditions

Complexity: highPlanning

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🎭

Scenario-Based Planning(SBP)

Multi-scenario planning that prepares for different possible futures and uncertain conditions

Complexity: highPlanning

Loading technique guide…

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Agentic DesignPatterns and tools for production AI agents

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Design Patterns & Techniques

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🎭

Scenario-Based Planning(SBP)

Multi-scenario planning that prepares for different possible futures and uncertain conditions

Complexity: highPlanning

Core Mechanism

Scenario Planning develops robust strategies for agentic AI systems by exploring multiple plausible futures under uncertainty. Teams identify key drivers and critical uncertainties, construct a small set of contrasting yet plausible scenarios, stress-test plans against each scenario, and define early-warning indicators and decision triggers for timely adaptation.

Workflow / Steps

Frame the decision: clarify scope, planning horizon, stakeholders, constraints, and success criteria.
Environmental scanning: collect signals and trends; identify key drivers and critical uncertainties.
Scenario construction: craft 3–4 plausible, internally consistent scenarios covering the uncertainty space.
Implications analysis: derive risks, opportunities, and operational constraints for each scenario.
Strategy design: specify core strategies that work across scenarios and options specific to each.
Decision triggers: define leading indicators, thresholds, and playbooks for scenario shifts.
Pilot and portfolio: stage investments, build optionality, and run controlled experiments.
Monitoring and refresh: track indicators, review quarterly/biannually, and update scenarios/strategies.

Best Practices

Limit to 3–4 contrasting, plausible scenarios; avoid probability debates early; ensure internal consistency.

Represent heterogeneous perspectives (product, safety, legal, ops, finance, user research) to reduce bias.

Make scenarios decision-useful: quantify ranges, surface constraints, and tie to concrete actions.

Define a robust core strategy plus option-specific hedges; pre-approve playbooks for fast pivots.

Instrument early-warning indicators and automate ingestion where possible (dashboards, alerts).

Rehearse transitions with tabletop exercises; maintain a living scenario log and postmortems.

Integrate with AI risk frameworks (NIST AI RMF, ISO/IEC 23894) and regulatory watch (e.g., EU AI Act).

When NOT to Use

Immediate, tactical decisions with tight real-time SLOs where foresight workshops add latency.
Domains with low uncertainty and stable, well-understood dynamics where standard forecasting suffices.
Situations lacking the organizational will to act on outcomes (no appetite for portfolio options).

Common Pitfalls

Over-indexing on a “most likely” scenario and underinvesting in hedges and leading indicators.
Producing narrative-only scenarios without quantitative bounds, triggers, or executable playbooks.
Too many scenarios causing analysis paralysis; or scenarios that are not mutually distinct.
Failure to refresh scenarios as the environment and model capabilities change.

Key Features

Plausible, internally consistent futures (3–4)

Explicit critical uncertainties and key drivers

Core strategy + scenario-specific options

Leading indicators and decision triggers

Playbooks for fast transitions

Portfolio and staged investments

KPIs / Success Metrics

Decision readiness: coverage of critical uncertainties; playbook completeness and time-to-pivot.
Adaptation speed: detection-to-action latency when indicators cross thresholds.
Outcome resilience: performance variance across scenarios (cost, safety, compliance, quality).
Review cadence adherence and scenario refresh rate.

Token / Resource Usage

LLM budgeting: 3–4 scenarios × (drivers + uncertainties + narrative + implications + strategy) tokens.
Prefer structured prompts and tables; summarize background (10–20%) and reference sources by link/id.
Automate indicator monitoring to reduce manual analysis; cache canonical scenario templates.
Estimate cost per review cycle; cap depth of exploration per session and batch long reports.

Best Use Cases

AI product roadmaps under regulatory and capability uncertainty (e.g., EU AI Act timelines, model advances).
Risk management for safety-critical deployments (healthcare, finance, autonomous systems).
Go-to-market and pricing under macro uncertainty; data residency/compliance strategies.
Org capability building and investment portfolio planning for agentic platforms.

References & Further Reading

Academic Papers

Implementation Guides

Tools & Libraries

EMA Workbench (Exploratory Modeling & Analysis)
SALib (Sensitivity Analysis) / Mesa (Agent‑Based Modeling)
Vensim / System Dynamics and AnyLogic for simulation
LangGraph and orchestration frameworks for planner–executor loops

Community & Discussions

Association of Professional Futurists (APF) and OECD.AI
WEF C4IR policy/foresight resources
Hugging Face Forums (AI governance and deployment threads)

Loading technique guide…

🎭

Scenario-Based Planning(SBP)

Multi-scenario planning that prepares for different possible futures and uncertain conditions

Complexity: highPlanning

Core Mechanism

Workflow / Steps

Frame the decision: clarify scope, planning horizon, stakeholders, constraints, and success criteria.
Environmental scanning: collect signals and trends; identify key drivers and critical uncertainties.
Scenario construction: craft 3–4 plausible, internally consistent scenarios covering the uncertainty space.
Implications analysis: derive risks, opportunities, and operational constraints for each scenario.
Strategy design: specify core strategies that work across scenarios and options specific to each.
Decision triggers: define leading indicators, thresholds, and playbooks for scenario shifts.
Pilot and portfolio: stage investments, build optionality, and run controlled experiments.
Monitoring and refresh: track indicators, review quarterly/biannually, and update scenarios/strategies.

Best Practices

Limit to 3–4 contrasting, plausible scenarios; avoid probability debates early; ensure internal consistency.

Represent heterogeneous perspectives (product, safety, legal, ops, finance, user research) to reduce bias.

Make scenarios decision-useful: quantify ranges, surface constraints, and tie to concrete actions.

Define a robust core strategy plus option-specific hedges; pre-approve playbooks for fast pivots.

Instrument early-warning indicators and automate ingestion where possible (dashboards, alerts).

Rehearse transitions with tabletop exercises; maintain a living scenario log and postmortems.

Integrate with AI risk frameworks (NIST AI RMF, ISO/IEC 23894) and regulatory watch (e.g., EU AI Act).

When NOT to Use

Immediate, tactical decisions with tight real-time SLOs where foresight workshops add latency.
Domains with low uncertainty and stable, well-understood dynamics where standard forecasting suffices.
Situations lacking the organizational will to act on outcomes (no appetite for portfolio options).

Common Pitfalls

Over-indexing on a “most likely” scenario and underinvesting in hedges and leading indicators.
Producing narrative-only scenarios without quantitative bounds, triggers, or executable playbooks.
Too many scenarios causing analysis paralysis; or scenarios that are not mutually distinct.
Failure to refresh scenarios as the environment and model capabilities change.

Key Features

Plausible, internally consistent futures (3–4)

Explicit critical uncertainties and key drivers

Core strategy + scenario-specific options

Leading indicators and decision triggers

Playbooks for fast transitions

Portfolio and staged investments

KPIs / Success Metrics

Decision readiness: coverage of critical uncertainties; playbook completeness and time-to-pivot.
Adaptation speed: detection-to-action latency when indicators cross thresholds.
Outcome resilience: performance variance across scenarios (cost, safety, compliance, quality).
Review cadence adherence and scenario refresh rate.

Token / Resource Usage

LLM budgeting: 3–4 scenarios × (drivers + uncertainties + narrative + implications + strategy) tokens.
Prefer structured prompts and tables; summarize background (10–20%) and reference sources by link/id.
Automate indicator monitoring to reduce manual analysis; cache canonical scenario templates.
Estimate cost per review cycle; cap depth of exploration per session and batch long reports.

Best Use Cases

AI product roadmaps under regulatory and capability uncertainty (e.g., EU AI Act timelines, model advances).
Risk management for safety-critical deployments (healthcare, finance, autonomous systems).
Go-to-market and pricing under macro uncertainty; data residency/compliance strategies.
Org capability building and investment portfolio planning for agentic platforms.

References & Further Reading

Academic Papers

Implementation Guides

Tools & Libraries

EMA Workbench (Exploratory Modeling & Analysis)
SALib (Sensitivity Analysis) / Mesa (Agent‑Based Modeling)
Vensim / System Dynamics and AnyLogic for simulation
LangGraph and orchestration frameworks for planner–executor loops

Community & Discussions

Association of Professional Futurists (APF) and OECD.AI
WEF C4IR policy/foresight resources
Hugging Face Forums (AI governance and deployment threads)

Loading technique guide…

Patterns

closed

Design Patterns & Techniques

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Core Mechanism

Workflow / Steps

Frame the decision: clarify scope, planning horizon, stakeholders, constraints, and success criteria.
Environmental scanning: collect signals and trends; identify key drivers and critical uncertainties.
Scenario construction: craft 3–4 plausible, internally consistent scenarios covering the uncertainty space.
Implications analysis: derive risks, opportunities, and operational constraints for each scenario.
Strategy design: specify core strategies that work across scenarios and options specific to each.
Decision triggers: define leading indicators, thresholds, and playbooks for scenario shifts.
Pilot and portfolio: stage investments, build optionality, and run controlled experiments.
Monitoring and refresh: track indicators, review quarterly/biannually, and update scenarios/strategies.

Best Practices

Limit to 3–4 contrasting, plausible scenarios; avoid probability debates early; ensure internal consistency.

Represent heterogeneous perspectives (product, safety, legal, ops, finance, user research) to reduce bias.

Make scenarios decision-useful: quantify ranges, surface constraints, and tie to concrete actions.

Define a robust core strategy plus option-specific hedges; pre-approve playbooks for fast pivots.

Instrument early-warning indicators and automate ingestion where possible (dashboards, alerts).

Rehearse transitions with tabletop exercises; maintain a living scenario log and postmortems.

Integrate with AI risk frameworks (NIST AI RMF, ISO/IEC 23894) and regulatory watch (e.g., EU AI Act).

When NOT to Use

Immediate, tactical decisions with tight real-time SLOs where foresight workshops add latency.
Domains with low uncertainty and stable, well-understood dynamics where standard forecasting suffices.
Situations lacking the organizational will to act on outcomes (no appetite for portfolio options).

Common Pitfalls

Over-indexing on a “most likely” scenario and underinvesting in hedges and leading indicators.
Producing narrative-only scenarios without quantitative bounds, triggers, or executable playbooks.
Too many scenarios causing analysis paralysis; or scenarios that are not mutually distinct.
Failure to refresh scenarios as the environment and model capabilities change.

Key Features

Plausible, internally consistent futures (3–4)

Explicit critical uncertainties and key drivers

Core strategy + scenario-specific options

Leading indicators and decision triggers

Playbooks for fast transitions

Portfolio and staged investments

KPIs / Success Metrics

Decision readiness: coverage of critical uncertainties; playbook completeness and time-to-pivot.
Adaptation speed: detection-to-action latency when indicators cross thresholds.
Outcome resilience: performance variance across scenarios (cost, safety, compliance, quality).
Review cadence adherence and scenario refresh rate.

Token / Resource Usage

LLM budgeting: 3–4 scenarios × (drivers + uncertainties + narrative + implications + strategy) tokens.
Prefer structured prompts and tables; summarize background (10–20%) and reference sources by link/id.
Automate indicator monitoring to reduce manual analysis; cache canonical scenario templates.
Estimate cost per review cycle; cap depth of exploration per session and batch long reports.

Best Use Cases

AI product roadmaps under regulatory and capability uncertainty (e.g., EU AI Act timelines, model advances).
Risk management for safety-critical deployments (healthcare, finance, autonomous systems).
Go-to-market and pricing under macro uncertainty; data residency/compliance strategies.
Org capability building and investment portfolio planning for agentic platforms.

References & Further Reading

Academic Papers

Implementation Guides

Tools & Libraries

EMA Workbench (Exploratory Modeling & Analysis)
SALib (Sensitivity Analysis) / Mesa (Agent‑Based Modeling)
Vensim / System Dynamics and AnyLogic for simulation
LangGraph and orchestration frameworks for planner–executor loops

Community & Discussions

Association of Professional Futurists (APF) and OECD.AI
WEF C4IR policy/foresight resources
Hugging Face Forums (AI governance and deployment threads)

Core Mechanism

Workflow / Steps

Frame the decision: clarify scope, planning horizon, stakeholders, constraints, and success criteria.
Environmental scanning: collect signals and trends; identify key drivers and critical uncertainties.
Scenario construction: craft 3–4 plausible, internally consistent scenarios covering the uncertainty space.
Implications analysis: derive risks, opportunities, and operational constraints for each scenario.
Strategy design: specify core strategies that work across scenarios and options specific to each.
Decision triggers: define leading indicators, thresholds, and playbooks for scenario shifts.
Pilot and portfolio: stage investments, build optionality, and run controlled experiments.
Monitoring and refresh: track indicators, review quarterly/biannually, and update scenarios/strategies.

Best Practices

Limit to 3–4 contrasting, plausible scenarios; avoid probability debates early; ensure internal consistency.

Represent heterogeneous perspectives (product, safety, legal, ops, finance, user research) to reduce bias.

Make scenarios decision-useful: quantify ranges, surface constraints, and tie to concrete actions.

Define a robust core strategy plus option-specific hedges; pre-approve playbooks for fast pivots.

Instrument early-warning indicators and automate ingestion where possible (dashboards, alerts).

Rehearse transitions with tabletop exercises; maintain a living scenario log and postmortems.

Integrate with AI risk frameworks (NIST AI RMF, ISO/IEC 23894) and regulatory watch (e.g., EU AI Act).

When NOT to Use

Immediate, tactical decisions with tight real-time SLOs where foresight workshops add latency.
Domains with low uncertainty and stable, well-understood dynamics where standard forecasting suffices.
Situations lacking the organizational will to act on outcomes (no appetite for portfolio options).

Common Pitfalls

Over-indexing on a “most likely” scenario and underinvesting in hedges and leading indicators.
Producing narrative-only scenarios without quantitative bounds, triggers, or executable playbooks.
Too many scenarios causing analysis paralysis; or scenarios that are not mutually distinct.
Failure to refresh scenarios as the environment and model capabilities change.

Key Features

Plausible, internally consistent futures (3–4)

Explicit critical uncertainties and key drivers

Core strategy + scenario-specific options

Leading indicators and decision triggers

Playbooks for fast transitions

Portfolio and staged investments

KPIs / Success Metrics

Decision readiness: coverage of critical uncertainties; playbook completeness and time-to-pivot.
Adaptation speed: detection-to-action latency when indicators cross thresholds.
Outcome resilience: performance variance across scenarios (cost, safety, compliance, quality).
Review cadence adherence and scenario refresh rate.

Token / Resource Usage

LLM budgeting: 3–4 scenarios × (drivers + uncertainties + narrative + implications + strategy) tokens.
Prefer structured prompts and tables; summarize background (10–20%) and reference sources by link/id.
Automate indicator monitoring to reduce manual analysis; cache canonical scenario templates.
Estimate cost per review cycle; cap depth of exploration per session and batch long reports.

Best Use Cases

AI product roadmaps under regulatory and capability uncertainty (e.g., EU AI Act timelines, model advances).
Risk management for safety-critical deployments (healthcare, finance, autonomous systems).
Go-to-market and pricing under macro uncertainty; data residency/compliance strategies.
Org capability building and investment portfolio planning for agentic platforms.