calle-ai/awesome-phone-call-agents/call-correction-propagation-auditor

call-correction-propagation-auditor

**A mid-call correction that never reaches the record is a silent lie

the summary tells.**

Every CALL-E application downstream of a call parses the agent-written post_summary blindly: dates feed writebacks, times feed calendars, amounts feed charges. Agents correct themselves mid-call all the time - "Sorry, I said Tuesday; I meant Thursday" - and conversation-analysis work shows speakers prefer exactly this self-repair form. But intrinsic self-correction is unreliable: an agent that fixed its own error in speech may still write the pre-correction value into the record, because a later correction does not automatically invalidate the earlier stored value in an LLM's context. The sibling call-post-summary-faithfulness-auditor anchors each summary claim to ANY occurrence in the transcript - so a summary carrying the PRE-correction "Tuesday" still grades SUPPORTED, because "Tuesday" was genuinely spoken. That is this skill's companion blind spot: faithfulness to the transcript is not faithfulness to the correction. This skill follows each agent self-correction chain and verifies that the final corrected value - not the superseded one - reached the summary, before automation acts on the stale one.

When To Use

• before any writeback, calendar entry, or outcome parse acts on the

post_summary of a call where the agent restated or corrected details

• after any call whose transcript shows the agent revising a date, time,

amount, count, or address mid-conversation

• combined with call-post-summary-faithfulness-auditor: run both - the

faithfulness auditor anchors claims correction-agnostically, this skill grades what happened to each correction

When Not To Use

• when the CALLEE revised their answer ("Actually, make it Friday") -

that is answer instability, the object of the provenance-grade skill

• when the CALLEE initiated the repair ("No, Thursday") - callee-side

other-repair signals belong to call-repair-sequence-auditor

• for semantic repair analysis; marker detection is lexical, so a

paraphrased correction can go undetected

• on masked spans; phone digits are masked before analysis by design and

are unverifiable by the same token

Verdicts

How It Works

Five deterministic stages, offline, no LLM:

1. Mask first. Any 7+-digit run (separators included) is masked in

the summary and every turn before any correction work, keeping the last two characters. Phone numbers can never anchor and never leak into cards.

1. Detect. Agent-side sentences (questions excluded) are scanned for

correction markers - sorry_i_said, meant, not_x_but_y, y_not_x, correction, let_me_correct, incorrect, should_be, my_mistake, actually_its, scratch - the first matching marker wins the sentence. Each marker binds an old and a new value of one kind (date, weekday, clock, money, count, street).

1. Build chains. Events whose old_value supersedes a chain's final

or superseded value extend that chain; otherwise they start a new one

• "Tuesday -> Thursday -> Friday" becomes one chain with two

superseded values.

1. Check propagation. Each chain's final and superseded values are

folded (case, meridiem to 24h, month names, digit commas) and searched in the folded summary with boundary guards ("14:00" does not match "2:14:00", "tuesday" does not match "tuesday's"). Superseded-in-summary-without-final is stale; both is ambiguous; final-only is propagated; neither is unreported.

1. Confirmation window. A chain is confirmed when any later agent

turn restates the final value, or a callee turn within turns i+1..i+2 after the correction repeats it or acknowledges ("ok", "that works", "sounds good", ...). Unconfirmed chains cap the verdict at CORRECTIONS_UNCONFIRMED unless a stale value already blocks it.

The card reports per-event corrections, per-chain summary checks, counts, an advisories list (summary_missing, unreported_chain: <value>), and a fixed honesty disclaimer.

Craft: the correction-discipline goal

python3 scripts/correction_propagation_auditor.py craft

Emits a plan_call goal whose CORRECTION DISCIPLINE block instructs the agent - the moment it corrects any detail - to re-state the corrected value in a full sentence, ask the caller to confirm it, and use only the corrected value thereafter, so the summary this skill later audits carries corrections forward instead of stale values.

Limitations

• English correction lexicons only; corrections phrased outside the

marker list go undetected

• spelled-out numbers ("four", "twelve") are not correctable values;

this is documented as not graded; never guessed, not silently graded

• masked digit runs (phones, long numbers) are never correctable by

design

• proximity resolution picks the nearest preceding same-kind value, so

disjunctive lists ("Tuesday or Wednesday") may bind the wrong superseded value

• corrections packed with multiple value pairs in one sentence ("it's the

21st, not the 16th, and 2 bags, not 3") may undercount - each marker binds one old/new pair

• weekday possessives and plurals ("Tuesday's") are treated as different

tokens by the boundary rule and will not match a plain "Tuesday"

• agent corrections prompted by the CALLEE ("No, Thursday") are

other-corrections and out of scope

• the whole audit is lexical, not semantic; a paraphrased correction or

summary restatement can evade detection

• the aggregate PROPAGATED label does not prove every correction reached

the summary; inspect each chain's summary check and the advisory list

Testing

python -m pytest scripts/test_correction_propagation_auditor.py -q
python3 scripts/test_correction_propagation_auditor.py

The suite in scripts/ covers masking, marker detection, chain building, propagation outcomes, the confirmation window, verdict routing, the CLI surface, and craft output against the bundled fixtures.

Scientific Foundation

Boundaries

• the provenance-grade skill grades CALLEE answer instability (its

signal I); this skill audits AGENT self-corrections and the summary record - the two directions do not overlap

• call-repair-sequence-auditor detects CALLEE-initiated other-repair

trouble signals; agent self-corrections are this skill's object

• call-post-summary-faithfulness-auditor anchors summary claims

correction-agnostically - any transcript occurrence satisfies it, so a stale summary can still grade FAITHFUL; they compose, run both

This skill is the deterministic, offline, lexical-marker variant of that methodology - not a learned model. Every output is labeled with a fixed disclaimer stating exactly that.