Agent Intent-First Index
The percentage of agent-authored PRs where CHI approval preceded the agent's first commit.
This page describes the intended design. It requires comparing agent-driven CHI approval timing against the agent's first commit, which isn't built yet. It doesn't appear on your dashboard yet, and no org currently has a live value for it.
CHI approval date
< agent first commit date
÷ total agent-authored PRs)
× 100
What it signals
The agentic equivalent of IFR. AIFI measures whether AI agents are being invoked against architect-approved Change Intentions before generating code — not after. A high AIFI means the orchestration layer ensures an approved CHI exists before the agent starts. A low AIFI means agents are autonomous code generators operating without pre-approved architectural scope: the highest-risk agentic deployment pattern.
IFR and AIFI look similar but require different remediation. Low human IFR is a culture and process problem — developers are not following the intent-first workflow. Low AIFI is an orchestration tooling problem — the system that spawns agents is not checking for approved CHI scope before dispatch. The fix is not developer education; it is workflow integration: require get_approved_chi() as a pre-start check in the orchestration layer.
AIFI rising after rkito MCP integration is evidence that the integration is working as intended. Agents invoked with an approved CHI in scope know what they are authorised to change before writing a single line. They operate as specification executors, not autonomous generators. This is the distinction that separates governed agentic development from ungoverned agentic development.
How rkito produces it
Agent-authored PR attribution uses commit signatures and MCP access logs to identify agent-generated branches. CHI approval timestamps are then compared against the earliest agent commit on that branch. AIFI is the percentage of agent-authored structural PRs that are AIFI HITs, computed per sprint.
Who this metric is for
AIFI shows whether the team uses agents as specification executors (high AIFI) or autonomous generators (low AIFI). These are fundamentally different risk profiles.
AIFI is the signal for whether agent sessions start with approved design context. Low AIFI = agents are making architectural decisions without review.
Watch
Do not optimise AIFI by creating placeholder CHIs that get immediately approved before agent dispatch without genuine architectural review. A CHI approved in under 2 minutes with no reviewer comments just to satisfy the AIFI gate is form without governance. Track alongside CRPR (Comment Resolution Rate per Review).
Also: AIFI is meaningless if CHIs are trivially scoped — an approved CHI that covers “any changes to any file” satisfies AIFI technically but provides zero architectural governance.
What to do at each threshold
Step-by-step response playbook for each signal state.
Agents are being invoked within approved architectural scope. Confirm MCPCAR is also high — agents should be loading design context, not just having a CHI on record.
Confirm MCPCAR is also high — agents should be loading design context, not just having a CHI technically on record. A high AIFI with low MCPCAR means the pre-start check exists but the context load does not. The CHI is approved; the agent never read it.
Identify which agent workflows are starting without approved CHIs. Integrate the pre-start check in the orchestration layer.
Identify which agent workflows are starting without approved CHIs. Integrate get_approved_chi() as a mandatory pre-start check in the orchestration layer.
Agents are operating as autonomous generators without design scope. Require the orchestration layer to call draft_chi and halt if no approved CHI exists before agent startup.
Require the orchestration layer to call draft_chi and halt if no approved CHI exists before agent startup. This is an orchestration tooling problem, not a culture problem — the fix is a code change, not an announcement.
The human equivalent of AIFI. IFR measures whether developers begin structural work with an approved CHI. Low IFR is a culture problem; low AIFI is an orchestration tooling problem.
The depth signal that AIFI precedes. High AIFI means an approved CHI existed before the agent started — MCPCAR measures whether the agent actually loaded that context.
The output signal that AIFI predicts. Low AIFI structurally guarantees high ADRIFT — agents without approved scope will produce ungoverned architectural changes at machine speed.
What is your org's AIFI?
rkito calculates AIFI automatically from agent PR attribution, CHI approval timestamps, and first commit dates. Connect your repo and see your agentic governance posture within minutes.