Agentic AI

The Trade Policy Tracker: A Paradigm Shift in Monitoring

Our flagship development experiment, the Trade Policy Tracker, serves as a blueprint for the future of policy intelligence. Developed across both Microsoft Copilot and Google Gemini platforms, the agent utilizes a “hard-gated” architecture that prioritizes structural scope enforcement over soft guidance. By mandating deep-link verification to official government sources and applying semantic normalization to complex trade categories, we have solved for common AI failures such as “helpfulness bias” and “hallucination”. This system ensures that analysts receive only verifiable, well-structured data, allowing them to focus on high-level strategic interpretation rather than manual data gathering.

​Expansion: The CERE Agentic Development Roadmap

Building on the lessons learned from our Trade Policy Tracker, CERE is expanding its agentic development into four critical domains of economic analysis:

  • Autonomous Impact Evaluation for Private Sector Development: We are developing agents to streamline OECD-DAC compliant evaluations. These agents can autonomously ingest project documents, verify KPIs against ground-truth data, and generate initial performance assessments for SME development and startup acceleration projects, drastically reducing reporting latency.

  • Real-Time Food and Fertilizer Price Monitoring: Leveraging our experience with trade measures, these agents are designed to monitor global commodity exchanges and local market prices. By cross-referencing price spikes with newly detected trade policies, the system identifies market insulation trends and alerts stakeholders to emerging supply-chain risks.
  • Automated Household Survey Data Analysis: We are building agents capable of interfacing with large-scale datasets (such as LSMS) to perform automated diagnostic cleaning and analysis. These agents utilize semantic parsing to harmonize disparate survey codes, allowing for rapid generation of descriptive statistics and consumption profiles across multiple years and regions.
  • Integrated Poverty and Food Security Analysis (Agent-Linked CGE): In our most advanced development track, we aim to integrate Recursive Dynamic CGE models directly into the agentic workflow. This will allow an agent to detect a trade shock, automatically update model parameters, and run simulations to estimate the resulting impact on poverty levels and household food security without manual recalibration.

Strategic Insight for Your Roadmap

By framing these as “Agent-Linked” or “Autonomous” processes, you highlight that CERE isn’t just using AI to write about economics, but to execute the technical steps of a PhD-level economist—from data cleaning to CGE simulation

 

Transforming Policy into Development Impact with High Effective Use of AI Intelligent Systems.

Rigorous Economics. Real-World Impact.