Rate your employer's ability to operate large numbers of agents in production, including monitoring and exception handling

The mean rating was 3.7 and the median 4.0 (n = 798 respondents). Ratings of 3 or below, the distressed range, made up 47.49% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.59, 95% CI −0.68 to −0.47, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.70, 95% CI 0.58 to 0.79, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.87, 95% CI 0.83 to 0.91, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.61, cluster bootstrap 95% CI −0.67 to −0.53, p < 0.001 with standard errors clustered by engagement, n = 750 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 29.78, Holm p < 0.001, omega squared 0.333) and respondent role (Welch F 6.82, Holm p = 0.012, omega squared 0.016).

Of the 37 written responses, 29 were negative. The most common account was of agents that no one on the vendor's side watched while they ran, with failures discovered the next morning, by the financial institution or by its staff. Others described a firm able to run a handful of agents and unready for hundreds. The positive responses described monitoring that worked. A more negative written score came with higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 379 of the 798 answers (47.49%). The engagement mean fell in the distressed range in 64 of 150 engagements.

The primary responses numbered 30, all written in answer to C135, “Add any comments on your employer that the ratings in this section do not capture.” The remaining 7 responses, 18.92% of the total, were secondary and came from 3 other questions. That secondary share ranked 36th from the highest among the 71 Part 2 indicators with 30 or more written responses, against a median of 18.92%. Respondents who wrote both a primary and a secondary response numbered 1. Negative responses made up 76.67% of primary responses (23 of 30) and 6 of the 7 secondary responses.

Eleven responses said that no one on the vendor's side watched the agents while they ran, or that monitoring was improvised. Respondents found failures in the morning queue or the morning break count, from employees of the financial institution who complained, or from the institution itself. One team's monitoring of overnight runs was a messaging channel a team member had built. In another engagement two agents provisioning in the same tenant went unwatched, one looped and raised dozens of duplicate accounts, and the team wrote a runbook only afterward. A respondent with no one rostered for night failures kept a phone close at hand as the escalation path.

Eight responses said the firm could run a few agents and not many: comfortable with twelve and not two hundred, with forty and not four hundred, already manual at five. All 5 responses to C029 named the scaling of the agent fleet among the hardest challenges, in one case a fleet outrunning the operating model. The single response to C055 described an access log that recorded which agent opened a record and nothing of what it did next.

The eight positive responses described agents run at scale and watched properly. One operations desk caught an agent looping on a test endpoint over a weekend and stopped it within the hour, before the financial institution noticed. Several of these respondents placed the weakness elsewhere, in banking depth, in checking output that varies between runs, or in hiring.

Of the 37 responses, 29 (78.38%, 95% CI 62.86% to 91.89%) were negative and 12 (32.43%) carried the strongest negative score of −10. Positive responses numbered 8 (21.62%), and no response was neutral. The mean written score was −4.1 (95% CI −6.1 to −1.9, n = 37 responses), and the median was −5.0.

Of the 37 responses, 36 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.63, 95% CI 0.38 to 0.79, p < 0.001, n = 36 responses). The written responses corroborated the rating. Of the 28 negative responses with a rating, 15 (53.57%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 35, with a mean rating of 4.4. The 763 respondents who rated the indicator without writing about it had a mean of 3.7. The difference, 0.7 (95% CI −0.2 to 1.6), included zero. Ratings in the distressed range made up 42.86% of the ratings from respondents who wrote (15 of 35) and 47.71% of the ratings from respondents who did not (364 of 763).

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 29.78, Holm p < 0.001, omega squared 0.333). Active engagements in Build-Out rated highest (mean 4.5, n = 168 respondents) and canceled engagements lowest (mean 2.4, n = 297 respondents). Ratings also differed by respondent role (Welch F 6.82, Holm p = 0.012, omega squared 0.016). Portfolio directors rated highest (mean 4.9, n = 30 respondents) and individual contributors lowest (mean 3.6, n = 616 respondents). Outsourcing model, institution type, process family, class of work and vendor type showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 30 engagements with a written response (Kruskal-Wallis H 5.58, df 3, p = 0.134). Of the groups with 10 or more responses, responses from active engagements in Transition/Cut-Over were the most negative (mean −3.3, 12 responses) and responses from active engagements in Build-Out the least negative (mean −1.0, 10 responses). Responses from active engagements in Production (6) and canceled engagements (9) are reported as counts only.

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 24, engagement managers wrote 10 and portfolio directors wrote 3 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 = −0.59, 95% CI −0.68 to −0.47, 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 = 0.70, 95% CI 0.58 to 0.79, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in fifteen 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 = 0.87, 95% CI 0.83 to 0.91, 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 = −0.61, 95% CI −0.65 to −0.56, Holm p < 0.001, n = 750 respondents). The association held after Holm correction in thirty-six of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 125 other engagements, 120 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 25 engagements with a negative response, 9 (36.00%) were canceled, against 47 of the 125 other engagements (37.60%). The difference did not reach significance (Fisher exact p = 1.000). Customer satisfaction among active engagements did not differ between the two groups (mean 67.25% on 16 engagements against 63.45% on 78, Welch t 1.04, p = 0.309).

Two correlations test written sentiment directly. Across the 30 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.35, p = 0.055). Across 33 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.38, p = 0.029). Of the four 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 = 0.314).

Answers clustered within engagements (ICC 0.61). Clustering reduced the 798 respondent answers to the equivalent of about 220 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 0.87.

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, 78.38%, ranked 43rd from the highest, against a median of 80.82%. Its mean written score, −4.1, ranked 48th from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C123, “Rate the adequacy of your employer's investment in training its workforce.”, in 8 of the 37 responses (21.62%). C122, “Rate your employer's current supply of qualified agentic AI talent against what its engagements need.” (5 responses), C125, “Rate how well your employer's training programs build financial industry knowledge.” (5 responses), C120, “Rate the depth of your employer's knowledge of the financial industry.” (4 responses) and C157, “Rate the factual reliability of the AI tools' outputs on this engagement before any human review.” (4 responses) followed.

The most frequent theme was “Practice invented on the live engagement”, in 4 responses (10.81%). The second was “No out-of-hours or incident path”, in 4 responses.

Nearly half of all ratings of the employer's ability to operate large numbers of agents in production fell in the distressed range (379 of 798 answers, 47.49%). 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 2.4, n = 297 respondents).

The written evidence describes agents in production without an operations function around them: no one assigned to watch them, no alerting on the agents themselves and no route for a failure out of hours. Where such a function existed, respondents rated the capability high. Written sentiment was associated with intent to leave.

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 (37 responses on 30 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.