Rate how well the contracts with third-party providers deal with provider changes that can break the agents on this engagement

The mean rating was 4.7 and the median 5.0 (n = 799 respondents). Ratings of 3 or below, the distressed range, made up 32.04% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.64, 95% CI −0.73 to −0.54, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.67, 95% CI 0.54 to 0.77, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.86, 95% CI 0.82 to 0.90, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.66, cluster bootstrap 95% CI −0.72 to −0.59, p < 0.001 with standard errors clustered by engagement, n = 747 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 37.19, Holm p < 0.001, omega squared 0.401), respondent role (Welch F 8.51, Holm p = 0.003, omega squared 0.016) and vendor type (Welch F 3.12, Holm p = 0.048, omega squared 0.013).

Of the 21 written responses, most from C198, the comment field on the third-party ecosystem, 14 were negative and 7 positive. The negative responses said the contracts were silent on notice of change and on remedy when a change broke the agents, or that the delivery team held no contract with the provider at all. The positive responses described change and notice clauses added during the engagement. Respondents whose written responses were more negative reported higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 256 of the 799 answers (32.04%). The engagement mean fell in the distressed range in 41 of 150 engagements.

The primary responses numbered 17, all written in answer to C198, “Add any comments on the third-party ecosystem that the ratings in this section do not capture.” The remaining 4 responses, 19.05% of the total, were secondary and came from 3 other questions. Respondents who wrote both a primary and a secondary response numbered 0. Negative responses made up 58.82% of primary responses (10 of 17) and 4 of the 4 secondary responses.

Eight responses said the contracts said nothing, or nothing useful, about notice of change or remedy: no obligation on a registry vendor to notify interface changes, no remedy where a provider change broke the agents, and no written interface commitment or thirty-day notice. One respondent said word of a change travelled through people before any contract obliged anyone to tell the team. Six responses said the provider owed the delivery team nothing because the relationship sat with the financial institution. In one account, a custodian redesigned its login page, the agent stopped collecting statements, and the breaks fell to zero on the dashboard. For four days that read as an improvement, and when the team raised it, the financial institution said the custodian relationship was out of scope.

Two responses said the protections existed only on paper: contracts that covered provider changes did nothing on the day a model version moved, and a fallback that had never once been tested. One respondent said the team had been fortunate with stable platforms, and that fortune is not a control. The seven positive responses included six describing change and notice clauses added after an incident or at review, one of which had already saved the team once, while noting an unmapped chain of sub-providers and licenses written for named users.

Of the 21 responses, 14 (66.67%, 95% CI 47.62% to 85.71%) were negative and 9 (42.86%) carried the strongest negative score of −10. Positive responses numbered 7 (33.33%), and no response was neutral. The mean written score was −3.8 (95% CI −6.4 to −1.0, n = 21 responses), and the median was −5.0.

Of the 21 responses, 21 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.69, 95% CI 0.40 to 0.86, p < 0.001, n = 21 responses). The written responses corroborated the rating. Of the 14 negative responses with a rating, 7 (50.00%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 21, with a mean rating of 5.3. The 778 respondents who rated the indicator without writing about it had a mean of 4.6. The difference, 0.6 (95% CI −0.6 to 1.9), included zero. Ratings in the distressed range made up 33.33% of the ratings from respondents who wrote (7 of 21) and 32.01% of the ratings from respondents who did not (249 of 778).

Every figure in the segment tables is at respondent level. Segments with fewer than 30 respondents carry no interval. Segments with fewer than 10 report counts only.

Segment families recorded at engagement level were tested on engagement means. Respondent role and vendor type were tested on respondents. Where an expected count fell below 5, the test of the distressed share used Fisher's exact test for a two-by-two table, marked exact, or a Monte Carlo test, marked simulated. After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 37.19, Holm p < 0.001, omega squared 0.401). Active engagements in Transition/Cut-Over rated highest (mean 5.8, n = 229 respondents) and canceled engagements lowest (mean 3.2, n = 307 respondents). Ratings also differed by respondent role (Welch F 8.51, Holm p = 0.003, omega squared 0.016). Portfolio directors rated highest (mean 5.9, n = 28 respondents) and individual contributors lowest (mean 4.5, n = 619 respondents). Ratings also differed by vendor type (Welch F 3.12, Holm p = 0.048, omega squared 0.013). Pure-play agentic AI firms rated highest (mean 5.1, n = 69 respondents) and BPO specialists lowest (mean 4.2, n = 189 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 21 engagements with a written response (Kruskal-Wallis H 4.89, df 3, p = 0.180). Fewer than two groups held 10 or more responses, so the groups are reported as counts only: active engagements in Build-Out (5), active engagements in Transition/Cut-Over (6), active engagements in Production (3) and canceled engagements (7).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 4, engagement managers wrote 13 and portfolio directors wrote 4 responses, reported as counts only.

Cancellation rate is the share of engagements canceled by the study date, measured at engagement level (150 engagements, 56 canceled). Canceled engagements were rated after cancellation. The association is therefore a descriptive contrast and not a forecast of cancellation.

Across the study set, higher ratings came with less cancellation (r = −0.64, 95% CI −0.73 to −0.54, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in eleven of the 20 segment levels tested.

Customer satisfaction is the percentage recorded against each engagement. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

Across the active engagements, higher ratings came with higher customer satisfaction (r = 0.67, 95% CI 0.54 to 0.77, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in thirteen of the 16 segment levels tested.

The engagement indicator mean is the mean of an engagement's ratings across the rated indicators, measured at engagement level (150 engagements). The indicator is one of the rated indicators. Each comparison therefore uses the mean recomputed without it.

Higher ratings came with a higher engagement indicator mean (r = 0.86, 95% CI 0.82 to 0.90, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twenty-four of the 24 segment levels tested.

Intent to leave is the answer to C019, “How likely are you to leave your current job in the next 12 months?”, rated 1 to 10. It is measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

Respondents who gave higher ratings reported lower intent to leave (r = −0.66, 95% CI −0.70 to −0.61, Holm p < 0.001, n = 747 respondents). The association held after Holm correction in thirty-five of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 136 other engagements, 129 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 14 engagements with a negative response, 6 (42.86%) were canceled, against 50 of the 136 other engagements (36.76%). The difference did not reach significance (Fisher exact p = 0.773). The comparison of customer satisfaction was not computed, because one of the two groups held too few active engagements.

Two correlations test written sentiment directly. Across the 21 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.36, p = 0.108). Across 20 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.64, p = 0.003). Of the three comparisons, one reached significance at 0.05.

The four associations were compared in pairs on their engagement-level forms, each oriented so that a higher value is the better outcome, across the 150 engagements with all four measures. After Holm correction, five of the six pairs differed. The association with the engagement indicator mean was the strongest and the association with cancellation the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.783).

Answers clustered within engagements (ICC 0.64). Clustering reduced the 799 respondent answers to the equivalent of about 212 independent answers. Respondent-level intervals therefore used the cluster bootstrap. Respondent-level tests of intent to leave used standard errors that allow for clustering by engagement.

No association was equivalent to zero. Each of the four differed from zero. The detection floor shown is the study floor at 150 engagements. Intent to leave was tested on its effective sample size.

Each change was computed before rounding and may differ by 0.01 from the difference between the two printed values.

All 22 checks held. A check holds when its test falls on the same side of 0.05 as the primary test. Where the primary test falls below 0.05, the check must also keep the sign of the primary correlation. The thin-base check removes the engagements in any level of outsourcing model or process family with fewer than 30 engagements. Institution type is not used for that check, because every one of its levels has fewer than 30 engagements.

Each of the four measures was computed from data the indicator does not contain. Each result is therefore a finding and not an arithmetic consequence. The indicator is one of the rated indicators, and the engagement indicator mean was recomputed without it. The correction changed r by 0.00, from 0.87 to 0.86.

Each deviation was computed before rounding and may differ by 0.01 from the difference between the two printed values.

The comparison covered 150 engagements. The indicator followed the study's typical pattern on all four measures, and no deviation held after Holm correction.

Among the 71 Part 2 indicators with 30 or more written responses, the indicator's share of negative responses, 66.67%, ranked 63rd from the highest, against a median of 80.82%. Its mean written score, −3.8, ranked 52nd from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C181, “Rate the quality of advance notice third-party providers give before changes that can affect the agents on this engagement.”, in 7 of the 21 responses (33.33%). C195, “Rate how well third-party licenses on this engagement cover work done by agents rather than named human users.” (5 responses), C179, “Rate the quality of the fallback the engagement has ready when a critical third-party feed, platform, or model stops working.” (4 responses), C182, “Rate how reliably word of an upcoming third-party change reaches the people who operate the affected agents in time to act.” (4 responses) and C185, “Rate how well the engagement absorbs model version deprecations and retirements forced by model providers.” (4 responses) followed.

The most frequent theme was “The scale or question does not fit”, in 5 responses (23.81%). The second was “Human rework absorbs the failure”, in 2 responses.

About a third of all ratings of how well third-party contracts dealt with breaking changes fell in the distressed range (256 of 799 answers, 32.04%). The rating was associated with all four study measures, and every association held under each of the 22 robustness checks. Canceled engagements rated far below the three active stages (mean 3.2, n = 307 respondents).

The written evidence describes a contractual gap between three parties. The provider's obligations ran to the financial institution, the agents were run by the vendor, and neither agreement anticipated a provider change that would break an agent. Where clauses were added, one respondent reported that a clause had already helped. Written sentiment was associated with intent to leave, on a small base.

The evidence is the reported experience of the vendor's delivery teams, collected in a single cross-section. The associations reported here are not causal.

Tests by respondent role and by vendor type treated respondents as independent. The section on clustering reports how far clustering within engagements reduced the information in respondent-level figures.

Customer management executives gave 2 answers, a base of anecdote strength.

Shares of written sentiment are shares of responses and not of the study set of respondents. The written score takes five fixed values, so its mean and median are coarse. Responses clustered within engagements (21 responses on 21 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.

The written responses number fewer than 30, so their shares and the written-sentiment tests rest on a small base.