Methodology

Agentic Index tracks and compares 892 vendors offering AI agent platforms, autonomous workflow automation tools, AI coding agents, GTM and revenue agents, browser/computer-use agents, customer support agents, and enterprise operations agents. We define an "agentic AI product" as software where an AI model takes multi-step actions, uses tools, and operates with some degree of autonomy toward a goal, not just a chatbot or single-turn inference product. Products must be shipping or in documented public beta to be included. Research previews and unpublished internal tools are excluded unless they have a public waitlist or published documentation. Canonical inclusion requires three criteria: (1) publicly documented agentic functionality: evidence that the product takes multi-step autonomous actions, uses tools, or orchestrates workflows across systems; (2) a practical evaluation path: a self-serve trial, free tier, open-source install, or documented API/SDK that allows a buyer or developer to evaluate the product without a mandatory sales call; and (3) enough source specificity to avoid vague marketing: at least one primary source (official docs, pricing page, trust center, or official changelog) with concrete feature or deployment evidence. Products with only general-purpose AI assistant positioning, unclear category fit, weak governance documentation, or no practical public evaluation path are tracked on an internal watchlist and not included in the public directory.

All vendor data is sourced from publicly available materials including: • Vendor product pages and documentation • Public pricing pages • Published case studies and blog posts • Analyst reports and third-party reviews • Public GitHub repositories where relevant • LinkedIn and Crunchbase for firmographic data We do not rely on vendor-provided data submissions. We do not accept payment for inclusion or favorable positioning. Vendors are not contacted for data verification at this stage, though that may change.

The feature taxonomy is organized into six categories derived from the buyer-facing agentic AI capability rubric: • Action & orchestration: Integrations & Tool Calling, Workflow Orchestration, and Triggers & Channel Coverage. • Knowledge & context: Knowledge Grounding & RAG, and Memory & State Persistence. • Control & trust: Human Oversight & Guardrails, Security Identity & Governance, Observability & Auditability, and Deployment & Data Residency. • Solution readiness: Prebuilt Agents, Templates & Packs. • Platform extensibility: Model Flexibility & Routing, APIs/SDKs & MCP Extensibility, and Testing, Debugging & Optimization. • Specialist automation: Browser & Computer Use. The taxonomy covers 14 canonical features ranked by buyer importance (importanceRank 1–14). Each feature carries equal weight (1/14 ≈ 7.1%) in the coverage score calculation. Feature definitions are fixed at taxonomy publication; when a definition is refined, affected vendor records are re-verified and dated.

Each vendor receives a feature coverage score calculated from its 14 vendor-feature records. Scores use the following point values: • Full / Explicit (F) = 1.0 point: confirmed from primary sources. • Partial (P) = 0.5 points: limited or restricted capability documented. • No / Not documented (N) = 0.0 points: confirmed absent from public materials. • Unknown / Unspecified (U) = 0.0 points: not yet verified from primary sources. The coverage score is the sum of point values divided by 14 (maximum). This ranking reflects documented public-source coverage, not a subjective quality judgement. A high Unknown count depresses a vendor's score until research is completed. It does not indicate poor capability.

Each vendor-feature relationship is assigned one of four support levels: • Full / Explicit: We have confirmed from primary sources that the vendor supports this feature explicitly. • Partial: The vendor supports a limited or restricted version of the capability; a note describes the limitation. • No / Not documented: We have confirmed from primary sources that this capability is absent or not publicly documented as first-class. • Unknown / Unspecified: We have not yet verified this vendor-feature combination from primary sources. This is the default state and should not be interpreted as a definitive absence of capability. Full and No ratings require a source URL and a confidence level.

Individual vendor-feature records carry a confidence score: • High: Evidence is from official documentation, a live product demo, or a direct product screenshot. • Medium: Evidence is from a blog post, press release, or secondary source that quotes the vendor. • Low: Evidence is from third-party review, forum post, or other non-authoritative source. Vendor-level records also carry a confidence level reflecting overall data completeness. A vendor with many Unknown feature cells will have a lower overall confidence rating.

Each vendor record and vendor-feature record carries a lastVerifiedAt date. Data older than 6 months should be considered potentially stale in fast-moving categories. Records that have not yet been formally reviewed may lack a lastVerifiedAt date. The absence of a verification date means the data should be treated as a research starting point, not a definitive assessment.

The Agentic Index is not: • A subjective recommendation engine: rankings reflect documented public-source coverage, not editorial opinion. • A paid directory: vendors cannot pay for inclusion, placement, or score inflation. • A vendor-verified database: vendors do not approve their own entries. • A real-time monitor: data is updated periodically, not continuously. The index is designed as a structured research resource for buyers, analysts, and operators who need to evaluate the agentic AI landscape based on documented capabilities rather than marketing claims.