Formula
AHCR = DCS(agent-authored PRs)
− DCS(human-authored PRs)Thresholds
Agents at or above human baseline
Target stateAgents below human baseline
Alert — review MCP integrationSignificant conformance gap
Escalate — halt expansionReading it
AHCR is the single most strategically important agentic metric rkito produces. It answers the question every CTO will eventually face: “Is AI coding making our architecture better or worse than humans?” A positive AHCR means agents (with MCP context) are outperforming humans on design conformance. A negative AHCR means they are not — and by exactly how much.
No other tool produces AHCR. It requires both agent attribution (knowing which PRs were agent-authored) and Lens-based CDA evaluation simultaneously. Code quality tools have neither. Architecture diagramming tools have neither. AHCR is rkito-exclusive.
The expected trajectory: AHCR starts negative (agents without design context produce more drift than humans who know the system implicitly). After rkito MCP integration, AHCR trends toward zero then positive (agents loaded with Ledger state, approved CHIs, and Lens criteria produce more conformant code than humans relying on implicit knowledge). Positive AHCR post-MCP integration is the ROI proof.
How rkito produces it
Every CDA audit is tagged by authorship. DCS is computed independently for agent-authored and human-authored PRs. AHCR is the delta — a single signed number that grows positive as MCP integration matures and agents load design context before the first token of code is generated.
- 01
Every CDA audit tagged: agent-authored or human-authored
From commit signatures (Co-Authored-By: Claude Code, Copilot metadata, Devin session ID) and MCP access logs
- 02
DCS computed separately for each group
Agent DCS and human DCS calculated independently over the rolling 30-day window
- 03
AHCR = agent DCS minus human DCS
A single signed number: positive means agents are more conformant, negative means they are less
- 04POSITIVE
Positive AHCR
Agents more conformant than humans — MCP integration is delivering measurable ROI
- 05NEGATIVE
Negative AHCR
Agents less conformant than humans — MCP context gap or pre-MCP baseline comparison
- 06
Updated on 30-day rolling window, trended sprint-over-sprint on org dashboard
AHCR trajectory is as important as the absolute value — watch the direction
Who reads it
AHCR is the number that answers the board question: "Are our AI tools making the codebase better or worse?" Not a sentiment, not a survey — a computed delta between agent and human design conformance. A positive AHCR is the quantified ROI of AI coding investment.
AHCR is the effectiveness signal for MCP integration. AHCR turning positive after MCP deployment proves the integration is working — agents are loading design context and producing more conformant code than humans relying on implicit knowledge. AHCR trending negative post-deployment means the MCP context is incomplete or misconfigured.
How it misleads
What to do at each threshold
Step-by-step response playbook for each signal state.
Agents meeting or exceeding human baseline. If positive, quantify the margin as MCP ROI.
- If AHCR is positive, express the margin as a concrete number for leadership: “agents produce conformant code X points more reliably than human-authored PRs.” This is the MCP ROI case — quantify it before the next budget cycle.
- Monitor MCPCAR to confirm agents are actually loading design context. A positive AHCR with low MCPCAR is suspicious — either a measurement gap or agents are outperforming on a narrow slice of work.
Agents are meaningfully below human baseline. Find the pattern before diagnosing the cause.
- Concentrated in specific Lenses: those rules need more explicit loading in agent MCP context. The agents have not been given the criteria they need before code generation starts.
- Concentrated in specific agents: those agent configurations need MCP review. Distributed across all agents and Lenses: MCP integration may not be fully deployed.
Halt expansion of AI tool adoption scope until the gap is understood.
- Check MCPCAR first. If agents are not loading MCP context, deploy MCP integration — the gap is structural absence of design context, not an agent capability problem.
- If MCPCAR is high but AHCR is still deeply negative, check LSCR — agents may be loading Lenses but failing to produce conformant code despite having the criteria. That is a different problem requiring agent configuration review, not MCP deployment.