# Agenda Intelligence: Building a Trustworthy Geopolitical Intelligence Layer for Agent Workflows

> An MCP server product that provides auditable strategic risk assessment capabilities for agent workflows through structured request/memo contracts, geographic routing reasoning, and evidence auditing, addressing the gaps of general LLMs in evidence discipline.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-22T11:45:19.000Z
- 最近活动: 2026-05-22T11:57:08.310Z
- 热度: 163.8
- 关键词: Agenda Intelligence, MCP服务器, 地缘政治情报, 智能体工作流, 证据审计, 结构化契约, 战略风险评估, LLM约束, 可审计AI, JSON Schema
- 页面链接: https://www.zingnex.cn/en/forum/thread/agenda-intelligence
- Canonical: https://www.zingnex.cn/forum/thread/agenda-intelligence
- Markdown 来源: floors_fallback

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## Introduction: Agenda Intelligence—Building a Trustworthy Geopolitical Intelligence Layer for Agent Workflows

Core Idea: Agenda Intelligence is an MCP server product designed specifically for agent workflows. It enhances the credibility and auditability of LLM outputs through structured request/memo contracts, geographic routing reasoning, and evidence auditing mechanisms, addressing the gaps of general LLMs in evidence discipline and providing auditable strategic risk assessment capabilities.

## Background: Limitations of General LLMs in Intelligence Analysis

General Large Language Models (LLMs) are powerful in information synthesis and text generation, but they have fundamental flaws in professional intelligence analysis: lack of evidence discipline, inability to verify sources, susceptibility to hallucinations, and inconsistent output formats. These issues are particularly dangerous in high-risk scenarios like geopolitical risk assessment, potentially leading to major decision-making errors. Agenda Intelligence was created to address these problems.

## Project Architecture and Methodology

Agenda Intelligence adopts the "Product Shell" architecture pattern, with the core repository serving as an integration point connecting multiple specialized components:

| Layer | Repository | Role |
|------|------|------|
| Product Shell | agenda-intelligence-md | MCP server, request/memo pattern, geographic routing, evidence auditing, scoring |
| Reasoning Method | global-think-tank-analyst | Strategic risk reasoning contract, loaded as the default method for analyze |
| Vertical Expert | central-asia-caspian-hybrid-intelligence-skill | In-depth expertise in Central Asia/Caspian/Middle Corridor, routed by geography |
| Vertical Expert | gulf-middle-east-hybrid-intelligence-skill | In-depth expertise in Iran/GCC/Maritime Chokepoints, routed by geography |

This layered architecture allows users to automatically route to the corresponding domain experts based on the geographic region of the analysis topic, while maintaining a unified interface contract.

## Detailed Explanation of Core Components

1. MCP Product Shell: The analyze tool accepts structured requests conforming to agenda-request.schema.json, routes them to professionals by geography, and returns memos conforming to agenda-memo.schema.json, ensuring input/output predictability.
2. Markdown Protocol: Agenda-Intelligence.md defines the structured reasoning workflow for agents, standardizes analysis steps and output formats, and is both human-readable and machine-parsable.
3. JSON Schema System: Maintains a complete set of JSON Schemas, including agenda-brief.schema.json (briefing), evidence-pack.schema.json (evidence pack), evidence-audit.schema.json (evidence audit), etc.
4. CLI Toolset: Provides over 30 command-line tools, such as `doctor` (status report), `validate-brief` (schema validation), `score` (heuristic scoring), `audit-claims` (claim auditing), etc.
5. MCP Server: The stdio MCP server exposes 16 tools, covering the validation layer and product layer.

## Evidence Discipline and Source Policy

The core value of Agenda Intelligence lies in emphasizing evidence discipline and implementing strict source policies: each claim must have clear source labels (Axis A/B), and source requirements are defined for 12 categories. Key principles:
1. Not a factual verifier: Only checks structure and evidence labels, does not verify the objective truth of claims.
2. Not an autonomous news agent: Does not actively retrieve real-time information, only processes user-provided inputs.
3. Not a source reputation scorer: Does not evaluate source credibility, only verifies whether citations are correct.

## Evaluation and Use Cases

Evaluation: The project includes 20 source-supported test cases, which can be reproduced using the `agenda-intelligence bench` command:

| Metric | Value |
|------|------|
| Number of Cases | 20 |
| Average Score | 87.6/100 |
| Min/Max | 84/91 |
| Schema Validation Pass Rate | 100% |
| With Evidence Pack | 100% |
| With Claim-level Audit | 100% |
| Average Source Coverage | 14.8% |
| Cases with Source Coverage Gaps | 20 |
| Orphan Evidence Citations | 0 |

It should be noted that heuristic scores are uncalibrated and not validated against expert judgments. They assess structure, evidence labels, source coverage diagnosis, and decision readiness, not factual truth.

Use Cases:
- Quick Start: Install via `pip install agenda-intelligence-md` and use commands like `doctor`, `validate-brief`, `score`, etc.
- MCP Client Integration: Supports clients like Cursor; generate configuration via `agenda-intelligence mcp-config --client cursor` then call the tools.
- Full Analysis Tracking: Provides end-to-end analyze process examples with reproducible scripts.

## Limitations and Future Directions

Limitations: The system does not provide functions such as factual verification, autonomous information retrieval, source reputation scoring, replacement of analyst judgments, compliance/legal/financial consulting, etc.

Future Directions: Integrate real-time information retrieval (while maintaining evidence discipline), develop source reputation assessment modules, expand geographic coverage, establish a community best practices library, and develop a visual interface to lower the barrier to use.

## Conclusion: The Design Philosophy of Constraints Over Capabilities

Agenda Intelligence demonstrates how to leverage LLM capabilities while addressing their inherent limitations through architectural design. It does not make LLMs more "intelligent" but rather makes their outputs more reliable, auditable, and suitable for professional scenarios through structured constraints and validation mechanisms. This "constraints over capabilities" design pattern provides valuable references for AI applications in other fields, especially for enterprise intelligence analysis, risk assessment reports, multi-agent collaboration, compliance-sensitive scenarios, etc.