Larridin's Agent Effectiveness product captures live sessions from Claude Code, Codex, Cursor, and Cline, grades each one, and can open a PR against CLAUDE.md or a skills directory with suggested changes, real session-level depth, alongside its enterprise-wide spend and shadow-AI governance view. Tetriz's Harness Control Plane instead publishes an approved baseline directly to every engineer's machine and tracks per-engineer install status, and its AI ROI is a disclosed formula: cycle-time savings per merged PR, valued at loaded engineering cost, minus the AI Tax and total AI spend.
Larridin's Agent Effectiveness is a real step up from artifact-only measurement: it captures live sessions from Claude Code, Codex, Cursor, and Cline, grades each one against its own dimensions, links sessions to the PRs they produced, and can open a PR against CLAUDE.md or a skills directory with suggested changes. Larridin also markets a headline dollar-return figure (a reported $2.4M in median AI spend optimized) across its enterprise governance surface. Tetriz's model differs in mechanism, not just claim: AI ROI is a disclosed formula, cycle-time savings per merged PR valued at loaded engineering cost, minus the AI Tax and total AI spend, and the Harness Control Plane publishes an approved baseline directly to every engineer's machine, tracking install status without needing a human to review and merge a PR first.
| Capability | Tetriz | Larridin |
|---|---|---|
| AI ROI methodology | AI ROI is a disclosed formula: cycle-time savings per merged PR, valued at loaded engineering cost, minus the AI Tax and total AI spend, an org-level number a leader can audit. | Markets a headline dollar-return figure (a reported $2.4M in median AI spend optimized) across its enterprise governance surface; the formula behind that number isn't published. |
| Recoverable spend & individual coaching | Every abandoned or no-ship session's spend is flagged as a likely candidate for prompt coaching or seat right-sizing, with a drilldown into the exact sessions and repos behind it, visible to the engineer who ran those sessions and their org's admins. | Synthesizes session feedback into a prioritized list of fixes; its public pages don't describe routing that as a private signal to the individual engineer. |
| Weekly coaching loop | The engineer who ran the session gets their own Session Quality Score every week, scored across six prompt-quality dimensions, visible only to them and their org's admins, never a cross-engineer ranking. | Agent Effectiveness grades sessions against its own dimensions and links them to the PRs they produced; there's no published equivalent of an individual weekly score restricted to the engineer and their admins. |
| Harness improvement & enforcement | A recommendation engine curates a vetted, pre-filtered base of skills, sub-agents, and rules; the Harness Control Plane pushes that baseline directly to every engineer's machine and reports back who has actually converged on it, no PR review required. | Agent Effectiveness can open a PR against CLAUDE.md or a skills directory with suggested changes, a real capability, but it's a suggestion a human must review and merge, not a baseline pushed and tracked automatically. |
A single view across spend, licence usage, shadow-AI discovery, and adoption spanning every department, not just engineering, backed by a $17M Andreessen Horowitz-led seed round and an Intellyx Digital Innovator Award, is real breadth and recognition Tetriz doesn't attempt to cover.
Agent Effectiveness captures live sessions from Claude Code, Codex, Cursor, Cline, and custom agents, links them directly to the PRs they produced, and can draft a harness-file PR from what it finds, a real, session-level capability.
Yes, and it's real: live sessions from Claude Code, Codex, Cursor, and Cline, graded and linked to the PRs they produced, with a suggested-PR flow for harness fixes. What it doesn't publish is the formula behind its headline ROI figures, and its harness fixes are a suggestion a human reviews rather than a baseline pushed and tracked automatically. Tetriz does both differently: a disclosed AI ROI formula, and a harness baseline the Control Plane pushes and tracks convergence on directly.
Not usually. Larridin's enterprise-wide spend governance and shadow-AI detection serve a different buyer (finance, security, compliance) than Tetriz's engineering-native ROI and coaching layer. Teams commonly run both.