SAGE: Agentic AI for Policy Design and Delivery
The Future Investment Initiative (FII) Institute introduced SAGE, an agentic AI platform for policymaking. It gives government teams a controlled way to design, test, and refine policies before they reach the public, with a clear aim: better services, fairer outcomes, and fewer unintended consequences.
Built with input from global AI experts, SAGE combines policy simulation, sovereign compute, and multilingual co-pilots configured for national contexts. The focus is practical-model the social and economic effects of a proposal, compare options, and move forward with evidence.
What SAGE brings to policy teams
- AI-powered policy simulation: Run scenarios, compare interventions, and stress-test assumptions against historical and synthetic data.
- Sovereign compute options: Keep sensitive workloads within national boundaries and under your controls.
- Multilingual co-pilots: Work in local languages, with context aware of laws, norms, and administrative structures.
- Impact modeling: Estimate distributional effects across regions, income levels, and demographics before rollouts.
- Citizen-first evaluation: Prioritize service quality, access, and equity-then tie those outcomes to measurable policy levers.
Why this matters for government
- Faster iteration: Move from white paper to pilot with fewer cycles and clearer trade-offs.
- Lower risk: Catch likely failure points early-budget overruns, implementation bottlenecks, or equity gaps.
- Evidence over opinion: Anchor debates in modeled outcomes and transparent assumptions.
- Auditability: Keep versioned simulations, datasets, and decision logs for oversight and public reporting.
Example workflow
- Define the policy goal and constraints (legal, fiscal, service-level).
- Import trusted datasets; document provenance and quality checks.
- Generate scenarios: status quo, reform A, reform B, and a hybrid option.
- Stress-test across economic conditions and demographic segments.
- Review projected outcomes, costs, and equity impacts; adjust parameters.
- Publish a policy brief with assumptions, methods, and anticipated outcomes.
Data governance and sovereignty
Many agencies need data residency, strict access controls, and model isolation. Sovereign compute options matter here-keep workloads on infrastructure you control, or within national providers that meet statutory requirements. Align risk controls with recognized frameworks such as the NIST AI Risk Management Framework and the OECD AI Principles.
Questions to ask before adoption
- Data protection: Where is data stored, who can access it, and how is it encrypted at rest and in transit?
- Model governance: How are models evaluated for bias, drift, and reliability? Are evaluations independent?
- Simulation validity: What methods and benchmarks support the forecasts? Can we reproduce results?
- Localization: How are legal, cultural, and administrative contexts configured and updated?
- Accessibility: Are co-pilots available in required languages and accessible formats?
- Costs: What are licensing, compute, and integration costs across departments?
- Exit strategy: Can we export data, prompts, and configuration to avoid lock-in?
90-day pilot plan
- Weeks 0-2: Scope one policy area, define KPIs, confirm data access, and set governance rules.
- Weeks 3-6: Build baseline and two alternative scenarios; run sensitivity and equity analyses.
- Weeks 7-10: Validate with stakeholders; conduct legal and security reviews; refine assumptions.
- Weeks 11-12: Publish a decision memo with methods, projected impact, and an implementation plan.
Metrics that matter
- Forecast accuracy vs. observed outcomes in pilots.
- Time to produce a policy brief and implementation plan.
- Equity indicators (e.g., service access by region or income quartile).
- Citizen satisfaction and complaint rates pre/post change.
- Cost to serve per case or per citizen interaction.
Risks and safeguards
- Bias in datasets: Use representative data and counterfactual testing; document limitations.
- Overfitting to history: Stress-test across future shocks, not just past patterns.
- Misuse of co-pilots: Role-based access, human-in-the-loop approvals, and activity logging.
- Dependency risk: Maintain internal capability and clear fallback processes.
Bottom line
SAGE gives policy teams a structured way to test choices before they touch people's lives. If you run pilots with clear guardrails, measure what matters, and keep humans accountable, you can improve service quality while spending public funds with greater confidence.
Build team capability
If you're setting up a policy AI workstream, upskilling your team speeds adoption and governance. See role-based learning options here: AI courses by job function.
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