diff --git a/DESCRIPTION b/DESCRIPTION
index dece4db5..e7a6b9e8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -64,7 +64,8 @@ Suggests:
     rpart,
     stringi,
     testthat (>= 3.0.0)
-Remotes: mlr-org/bbotk
+Remotes:
+    mlr-org/mlr3
 ByteCompile: no
 Encoding: UTF-8
 Config/testthat/edition: 3
diff --git a/R/mbo_defaults.R b/R/mbo_defaults.R
index 69c9664d..e6bcb7f1 100644
--- a/R/mbo_defaults.R
+++ b/R/mbo_defaults.R
@@ -161,14 +161,13 @@ default_surrogate = function(instance, learner = NULL, n_learner = NULL) {
       default_rf(noisy)
     }
     # stability: evaluate and add a fallback
-    learner$encapsulate[c("train", "predict")] = "evaluate"
     require_namespaces("ranger")
     fallback = mlr3learners::LearnerRegrRanger$new()
     fallback$param_set$values = insert_named(
       fallback$param_set$values,
       list(num.trees = 10L, keep.inbag = TRUE, se.method = "jack")
     )
-    learner$fallback = fallback
+    learner$encapsulate("evaluate", fallback)
 
     if (has_deps) {
       require_namespaces("mlr3pipelines")
@@ -184,8 +183,7 @@ default_surrogate = function(instance, learner = NULL, n_learner = NULL) {
           learner
         )
       )
-      learner$encapsulate[c("train", "predict")] = "evaluate"
-      learner$fallback = LearnerRegrFeatureless$new()
+      learner$encapsulate("evaluate", lrn("regr.featureless"))
     }
   }
 
diff --git a/tests/testthat/helper.R b/tests/testthat/helper.R
index 6acb90d0..41a886c8 100644
--- a/tests/testthat/helper.R
+++ b/tests/testthat/helper.R
@@ -101,13 +101,13 @@ MAKE_DESIGN = function(instance, n = 4L) {
 
 if (requireNamespace("mlr3learners") && requireNamespace("DiceKriging") && requireNamespace("rgenoud")) {
   library(mlr3learners)
-  REGR_KM_NOISY = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE, max.generations = 2), nugget.estim = TRUE, jitter = 1e-12)
-  REGR_KM_NOISY$encapsulate = c(train = "callr", predict = "callr")
-  REGR_KM_DETERM = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE, max.generations = 2), nugget.stability = 10^-8)
-  REGR_KM_DETERM$encapsulate = c(train = "callr", predict = "callr")
+  REGR_KM_NOISY = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE), nugget.estim = TRUE, jitter = 1e-12)
+  REGR_KM_NOISY$encapsulate("callr", lrn("regr.featureless"))
+  REGR_KM_DETERM = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE), nugget.stability = 10^-8)
+  REGR_KM_DETERM$encapsulate("callr", lrn("regr.featureless"))
 }
 REGR_FEATURELESS = lrn("regr.featureless")
-REGR_FEATURELESS$encapsulate = c(train = "callr", predict = "callr")
+REGR_FEATURELESS$encapsulate("callr", lrn("regr.featureless"))
 
 OptimizerError = R6Class("OptimizerError",
   inherit = OptimizerBatch,
diff --git a/tests/testthat/test_bayesopt_ego.R b/tests/testthat/test_bayesopt_ego.R
index ce2081a5..78cb4c11 100644
--- a/tests/testthat/test_bayesopt_ego.R
+++ b/tests/testthat/test_bayesopt_ego.R
@@ -109,7 +109,7 @@ test_that("stable bayesopt_ego", {
   expect_true(nrow(instance$archive$data) == 5L)
   expect_number(acq_function$surrogate$assert_insample_perf, upper = 1)
   lines = readLines(f)
-  # expect_true(sum(grepl("Optimizer Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
+  expect_true(sum(grepl("Optimizer Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
   expect_true(sum(grepl("Proposing a randomly sampled point", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 2L)
 
   # Surrogate using LearnerRegrError as Learner that will fail during train
diff --git a/tests/testthat/test_mbo_defaults.R b/tests/testthat/test_mbo_defaults.R
index 7cef2365..1c0fc4ba 100644
--- a/tests/testthat/test_mbo_defaults.R
+++ b/tests/testthat/test_mbo_defaults.R
@@ -23,7 +23,7 @@ test_that("default_surrogate", {
   expect_r6(surrogate$learner, "LearnerRegrKM")
   expect_equal_sorted(surrogate$learner$param_set$values,
     list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.stability = 1e-08))
-  expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+  expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
   expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
 
   # singlecrit all numeric, noisy
@@ -32,7 +32,7 @@ test_that("default_surrogate", {
   expect_r6(surrogate$learner, "LearnerRegrKM")
   expect_equal_sorted(surrogate$learner$param_set$values,
     list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.estim = TRUE, jitter = 1e-12))
-  expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+  expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
   expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
 
   # twocrit all numeric, deterministic
@@ -41,10 +41,10 @@ test_that("default_surrogate", {
   expect_list(surrogate$learner, types = "LearnerRegrKM")
   expect_equal_sorted(surrogate$learner[[1L]]$param_set$values,
     list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.stability = 1e-08))
-  expect_equal(surrogate$learner[[1L]]$encapsulate, c(train = "evaluate", predict = "evaluate"))
+  expect_equal(surrogate$learner[[1L]]$encapsulation, c(train = "evaluate", predict = "evaluate"))
   expect_r6(surrogate$learner[[1L]]$fallback, "LearnerRegrRanger")
   expect_equal(surrogate$learner[[1L]]$param_set$values, surrogate$learner[[2L]]$param_set$values)
-  expect_equal(surrogate$learner[[1L]]$encapsulate, surrogate$learner[[2L]]$encapsulate)
+  expect_equal(surrogate$learner[[1L]]$encapsulation, surrogate$learner[[2L]]$encapsulation)
   expect_equal(surrogate$learner[[1L]]$fallback, surrogate$learner[[2L]]$fallback)
 
   # twocrit all numeric, noisy
@@ -53,10 +53,10 @@ test_that("default_surrogate", {
   expect_list(surrogate$learner, types = "LearnerRegrKM")
   expect_equal_sorted(surrogate$learner[[1L]]$param_set$values,
     list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.estim = TRUE, jitter = 1e-12))
-  expect_equal(surrogate$learner[[1L]]$encapsulate, c(train = "evaluate", predict = "evaluate"))
+  expect_equal(surrogate$learner[[1L]]$encapsulation, c(train = "evaluate", predict = "evaluate"))
   expect_r6(surrogate$learner[[1L]]$fallback, "LearnerRegrRanger")
   expect_equal(surrogate$learner[[1L]]$param_set$values, surrogate$learner[[2L]]$param_set$values)
-  expect_equal(surrogate$learner[[1L]]$encapsulate, surrogate$learner[[2L]]$encapsulate)
+  expect_equal(surrogate$learner[[1L]]$encapsulation, surrogate$learner[[2L]]$encapsulation)
   expect_equal(surrogate$learner[[1L]]$fallback, surrogate$learner[[2L]]$fallback)
 
   # singlecrit mixed input
@@ -65,7 +65,7 @@ test_that("default_surrogate", {
   expect_r6(surrogate$learner, "LearnerRegrRanger")
   expect_equal_sorted(surrogate$learner$param_set$values,
     list(num.threads = 1L, num.trees = 100L, keep.inbag = TRUE, se.method = "jack"))
-  expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+  expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
   expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
 
   # twocrit mixed input
@@ -74,10 +74,10 @@ test_that("default_surrogate", {
   expect_list(surrogate$learner, types = "LearnerRegrRanger")
   expect_equal_sorted(surrogate$learner[[1L]]$param_set$values,
     list(num.threads = 1L, num.trees = 100L, keep.inbag = TRUE, se.method = "jack"))
-  expect_equal(surrogate$learner[[1L]]$encapsulate, c(train = "evaluate", predict = "evaluate"))
+  expect_equal(surrogate$learner[[1L]]$encapsulation, c(train = "evaluate", predict = "evaluate"))
   expect_r6(surrogate$learner[[1L]]$fallback, "LearnerRegrRanger")
   expect_equal(surrogate$learner[[1L]]$param_set$values, surrogate$learner[[2L]]$param_set$values)
-  expect_equal(surrogate$learner[[1L]]$encapsulate, surrogate$learner[[2L]]$encapsulate)
+  expect_equal(surrogate$learner[[1L]]$encapsulation, surrogate$learner[[2L]]$encapsulation)
   expect_equal(surrogate$learner[[1L]]$fallback, surrogate$learner[[2L]]$fallback)
 
   # singlecrit mixed input deps
@@ -152,12 +152,11 @@ test_that("stability and defaults", {
   # this should trigger a mbo_error
   instance = MAKE_INST_1D(terminator = trm("evals", n_evals = 5L))
   learner = LearnerRegrError$new()
-  learner$encapsulate[c("train", "predict")] = "evaluate"
-  learner$fallback = lrn("regr.ranger", num.trees = 10L, keep.inbag = TRUE, se.method = "jack")
+  learner$encapsulate("evaluate", lrn("regr.ranger", num.trees = 10L, keep.inbag = TRUE, se.method = "jack"))
   surrogate = default_surrogate(instance, learner = learner, n_learner = 1L)
   expect_r6(surrogate, "SurrogateLearner")
   expect_r6(surrogate$learner, "LearnerRegrError")
-  expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+  expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
   expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
   acq_function = default_acqfunction(instance)
   expect_r6(acq_function, "AcqFunctionEI")
@@ -172,17 +171,5 @@ test_that("stability and defaults", {
   lines = readLines(f)
   # Nothing should happen here due to the fallback learner
   expect_true(sum(grepl("Surrogate Train Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 0L)
-
-  acq_function$surrogate$learner$reset()
-  acq_function$surrogate$learner$fallback = NULL
-  instance$archive$clear()
-  bayesopt_ego(instance, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer)
-  expect_true(nrow(instance$archive$data) == 5L)
-  lines = readLines(f)
-  # Training fails but this error is not logged due to the "evaluate" encapsulate
-  expect_equal(acq_function$surrogate$learner$errors, "Surrogate Train Error.")
-  expect_true(sum(grepl("Surrogate Train Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 0L)
-  expect_true(sum(grepl("Cannot predict", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
-  expect_true(sum(grepl("Proposing a randomly sampled point", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
 })