ModelRefs / Approval Chains — Canonical Workflow

Approval Chains — Canonical Workflow

Approval Chains: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.

Overview

Orchestrate multi-step approval flows with policy-aware agents and full audit trails. Approval Chains is a provisional implementation reference with candidate models, providers, tools, benchmarks and deployment patterns to validate on the target workload. Operations deployment requires organization-specific access, retention, approval, exception, and audit controls. Integrations with ERP or workflow systems must be validated against the target data, permissions, and failure paths. Outputs remain provisional decision support and require accountable human review before consequential actions.

Implementation profile

Categoryagentic-models
Implementation maturityproduction
Evidence statusincomplete
Primary use casesenterprise-automation, agents
Deployment optionsmanaged-api, hybrid
Architecturesserverless-api, managed-container, hybrid-private-cloud

Candidate models with published references

Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.

Benchmarks relevant to this workflow

miracl, mkqa, mldr, swe-bench, aider-polyglot, gpqa, aime-2025, tau-bench, browsecomp-long-context, longfact-concepts, terminal-bench, mmmu, mmlu-pro, livecodebench.

Relevance is a coverage signal from the canonical registry. Each benchmark only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Approval Chains — Canonical Workflow.