Peer-Review Style Validation of Field Evidence Packages

Failed verification is a high-cost event in carbon markets, consuming weeks of rework, delaying revenue, and eroding project credibility. This workflow automates an internal, peer-review style validation to surface inconsistencies, missing documentation, and protocol misalignments before the formal audit. It deploys specialized AI agents—a Completeness Agent, a Consistency Agent, and a Protocol Agent—to critique evidence packages against a defined rubric, flagging issues for human remediation. This pre-flight check transforms verification from a reactive, high-stakes exam into a controlled, iterative quality process.

Implementation integrates with existing data lakes, farm management software, and document stores via APIs. The orchestrator, built on LangGraph, manages agent execution, evidence routing, and state. Each agent uses retrieval-augmented generation (RAG) against protocol documents and historical audits to ground its critiques. The workflow outputs a prioritized gap report and a readiness score, directly feeding into project management systems like Jira or Asana. This architecture provides the control and audit trail needed for regulated environments, turning validation into a repeatable, scalable operating procedure that protects verification economics.

This architecture automates the internal quality gate that determines if a soil carbon evidence package is verification-ready. It eliminates the bottleneck of manual, expert-led reviews that delay submissions and miss subtle inconsistencies. The operational upside comes from a 70-80% reduction in verification cycle rework, directly protecting project economics by accelerating credit issuance and lowering consultant fees. The system ingests fused data from remote sensing, farm management software (e.g., John Deere Operations Center), and lab results, then orchestrates a simulated peer-review process.

Implementation uses a LangGraph orchestrator to manage specialized agents, each trained on protocol documents (e.g., Verra VM0042) and historical audit findings. The Completeness Agent checks for missing temporal data or unsigned landowner contracts. The Protocol Alignment Agent validates practice definitions against methodology. The Consistency Agent cross-references satellite-detected tillage events against self-reported logs. A consensus engine scores the package; high-confidence approvals proceed, while exceptions are routed with specific remediation tasks to a human project manager in systems like Jira. Governance is enforced through immutable audit logs of all agent critiques, required for verifier due diligence.

This workflow automates the most critical bottleneck before third-party verification: the manual, expert-led review of complex evidence packages. It eliminates weeks of sequential human review by deploying specialized AI agents that critique data concurrently. The operational upside is a higher first-pass verification success rate, which directly reduces audit fees, shortens credit issuance cycles, and protects project revenue. The architecture is built on an orchestrator (e.g., LangGraph) that manages a council of agents, each trained on specific protocol domains like remote sensing validation, practice record logic, or statistical sampling compliance.

Implementation is phased, starting with a single protocol (e.g., Verra VM0042) and a core agent trio. The orchestrator routes the evidence package, each agent runs its checks against a knowledge base of protocol rules and historical audits, and a severity engine consolidates findings. Controls are essential: all critiques are logged for audit trails, and high-severity items mandate human review before auto-remediation. The final output is a verifier-ready package with a pre-verification readiness score, giving project developers quantified confidence and a clear remediation checklist.

This workflow directly targets the operational bottleneck of failed verification audits, which can delay credit issuance by months and incur six-figure rework costs. It automates an internal quality gate by deploying specialized AI agents that act as peer reviewers. One agent cross-references management records against satellite-detected practices, another validates soil sample chain-of-custody documentation, and a third checks all calculations against the specific methodology (e.g., Verra VM0042). This surfaces inconsistencies and missing evidence weeks before a verifier arrives, protecting project economics.

Implementation requires integrating with your existing data lake (remote sensing indices, ERP extracts) and document store. The orchestrator, built on LangGraph or CrewAI, manages agent execution, state, and the synthesis of findings into a readiness score. Critical controls include human-in-the-loop escalation for low-confidence flags and immutable audit logging of all agent critiques. Rollout is phased, starting with a single project type to tune agent logic before scaling across the portfolio. The result is a defensible, automated pre-audit that measurably reduces verification cycle time and risk.