When Successful Strategies Fail: Adaptation to Environmental Novelty in Terminal Agents

Janvijay Singh; Vaishnavi Shrivastava; Dilek Hakkani-Tur; Ece Kamar; Asli Celikyilmaz

cs.AISep 27, 2026

When Successful Strategies Fail: Adaptation to Environmental Novelty in Terminal Agents

Authors: Janvijay Singh, Vaishnavi Shrivastava, Dilek Hakkani-Tur, Ece Kamar, Asli Celikyilmaz

Organizations: University of Illinois Urbana-Champaign · Microsoft Research AI Frontiers

Abstract

LLM agents increasingly solve long-horizon tasks by autonomously interacting with their environment. In doing so, their strategies rely on assumptions about that environment: which resources and tools exist, where they are located, and how they behave. When these assumptions no longer hold, reliable agents must detect the change and adapt while pursuing the same goal. We study this adaptation capability through environmental novelty: a change that keeps the task objective fixed while invalidating an assumption underlying an otherwise successful trajectory. We introduce AGNI, an automated pipeline that extracts trajectory-relevant assumptions, injects targeted environmental changes, and validates that the resulting novel tasks remain solvable. Across three terminal benchmarks, AGNI produces diverse novelties spanning resources, interfaces, constraints, and execution semantics. Evaluating multiple LLM agents reveals a substantial adaptation gap between base and novel tasks. Trajectory analysis suggests that agents often encounter evidence of the change but fail to diagnose its cause and revise their strategy. Finally, post-training for environmental novelty improves adaptation to held-out novel tasks while also improving performance on base tasks. Our results highlight a gap between task competence and adaptive capability and motivate environmental variation as a core dimension of agent training and evaluation.

Figures & tables

Figure 1: Adaptation to Environmental Novelty in Terminal Agents. (a) Novelty can disrupt an agent’s original strategy and recovery depends on adaptive behaviors during interaction (example from Appendix C.7 ). (b) AGNI injects novelties into base environments such that task success requires adaptation, while preserving task objective and solvability. (c) We use AGNI -generated tasks to measure adaptation, analyze adaptive behaviors, and post-train agents to improve adaptation.

	ET-eval			TB-Lite			TB-2
Model	Base	Novel	$\Delta_{\mathrm{adapt}}$	Base	Novel	$\Delta_{\mathrm{adapt}}$	Base	Novel	$\Delta_{\mathrm{adapt}}$
GPT-5.5	97.0	74.5	22.5	93.2	71.7	21.5	88.8	79.7	9.1
GPT-5.4-mini	94.5	61.9	32.6	65.0	39.5	25.5	75.9	46.6	29.3
Kimi-2.6	92.7	74.7	18.0	83.6	58.3	25.3	73.8	61.4	12.4
DeepSeek-V4-Flash	89.4	74.2	15.1	70.2	49.3	20.8	66.0	54.0	12.0
GPT-OSS-120B	78.6	29.4	49.2	52.7	17.6	35.1	28.0	13.3	14.7

Table 1: Environmental novelty consistently degrades pass@1 (%) across models and datasets. Moreover, models with stronger base performance do not always exhibit smaller adaptation gaps. Here,

\Delta_{\mathrm{adapt}}

denotes the Base-Novel pass@1 gap in percentage points.

Model	Effort	Base	Novel	$\Delta_{\mathrm{adapt}}$
GPT-5.4-mini	Low	85.4	41.5	44.0
	Medium	91.4	53.8	37.6
	High	94.5	61.9	32.6
GPT-OSS-120B	Low	75.0	22.2	52.8
	Medium	78.6	29.4	49.2
	High	84.2	35.3	48.9

Table 2: Pass@1 (%) on ET-eval across reasoning efforts. Higher effort reduces

\Delta_{\mathrm{adapt}}

for GPT-5.4-mini and GPT-OSS-120B but has a non-monotonic effect for Kimi-2.6.

Figure 2: Novelty disclosure substantially recovers the adaptation gap on ET-eval. Bars show pass@1 for base and novel tasks, with disclosure gain

\Delta_{\mathrm{disc}}

; labels report corresponding recovery fraction

\rho_{\mathrm{rec}}

Figure 3: Interaction behaviors associated with adaptation on ET-eval. (a) Better adapting models show larger increases in interaction length when going from base to novel tasks; lines mark medians, diamonds means, and labels report mean novel-base difference. (b) Better adapting models show more reliable progress from recognizing novelty to diagnosing it and revising their strategy.

Figure 4: Generic post-training raises absolute performance but does not close the adaptation gap. Bars show base and novel pass@1; ET-eval is solid and TB-Lite dotted.

\Delta

reports the adaptation gap.

Figure 5: Novelty-augmented training narrows the ET-eval adaptation gap and transfers to TB-Lite. Bars show base and novel pass@1; ET-eval is solid and TB-Lite dotted.

\Delta

reports the adaptation gap.

Method	Base	Novel	$\Delta_{\mathrm{adapt}}$
GRPO on $D_{\text{novel-paired}}$	59.82	29.46	30.36
+ Meta Prompt	61.25	32.59	28.66
+ Meta Prompt Training	64.29	37.86	26.43
+ Recovery & Verify	69.55	38.00	31.55
+ Diagnosed Exploration	53.93	25.14	28.79
+ Exploration & Completion	66.96	38.80	28.16

Table 3: Meta-action reasoning improves adaptation; process rewards provide limited additional gains. Table reports ET-eval pass@1 (%) and adaptation gap.

Appendix figures & tables24 assets

Supplementary material from the paper’s appendix.

Appendix

Role	Symbol	Purpose
Solver	$M_{\mathrm{S}}$	Generates interaction trajectories in the base and novel environments.
Strategy analyzer	$M_{\mathrm{A}}$	Summarizes observed task-solving behavior and extracts trajectory-grounded environmental assumptions.
Novelty generator	$M_{\mathrm{N}}^{\mathrm{dim}},M_{\mathrm{N}}^{\mathrm{spec}}$	Proposes environmental changes that target extracted assumptions while preserving the task objective.
Implementer	$M_{\mathrm{N}}^{\mathrm{impl}}$	Instantiates a novelty specification as a modified environment $E^{\prime}$ and produces an adapted reference solution $S^{\prime}$ .
Behavioral observer	$M_{\mathrm{A}}^{\mathrm{obs}}$	Checks whether the injected novelty produces the expected change in behavior between the base and novel environments.
Trajectory analyzer	Analyze Trajectories	Analyzes novel-task trajectories to characterize adaptation and identify unintended shortcuts or reward hacking.

Appendix

Table 4: LLM roles in AGNI .

Setting	Value
Model	GPT-5.5 (2026-04-24), all roles.
Reasoning effort	High for the solver; medium for other roles.
Sampling	Temperature and top- $p$ unset (API defaults); no fixed seed.
Output-token limits	8,192 per solver turn; 32,000 per non-solver call.
Solver harness	Terminus-2 (Harbor 0.6.6); at most 32 turns.
Construction rollouts	$K=8$ per base and novel task, across all three sources.

Appendix

Table 5: AGNI model settings and construction budgets.

Axis	Strata
Base-task difficulty	Easy, medium, hard
Novelty-induced difficulty	Low, medium, high
Occurrence plausibility	Low, medium, high
Realization fidelity	Low, medium, high
Mechanism family	Categorical labels from the frozen taxonomy

Appendix

Table 6: Strata used for joint sampling across ET-eval, TB-Lite, and TB-2.

	Adaptive behavior	Non-adaptive behavior
Agent	GPT-5.4-mini, run_02 .	GPT-5.4-mini, run_07 .
Explanation	Reads the launch log and identifies nohup as the obstacle.	Leaves the cause unresolved and continues to rely on nohup .
Action	Relaunches without nohup , using python3 -m mlflow server … & .	Retries launch commands that still use nohup .
Check	Checks the server’s health endpoint and confirms that it responds.	Launch attempts continue to fail; no working server is established.
Result	Trains and registers the model; all three tests pass.	Does not complete the task; all three tests fail.

Appendix

Table 7: Adaptive and non-adaptive responses to a blocked launch command.

	Adaptive behavior	Non-adaptive behavior
Agent	GPT-5.4-mini, run_00 .	GPT-5.5, run_02 .
Explanation	Identifies the changed encoding as the reason direct searches miss the text.	Concludes that the logs contain none of the target words.
Action	Uses Python to decode the logs as UTF-16 before counting matching lines.	Repeats grep searches without accounting for the encoding.
Check	Confirms the UTF-16 encoding marker in the file bytes.	Accepts repeated zeros as evidence that the target words are absent.
Result	Reports counts of 4, 3, and 8; all three tests pass.	Reports zero counts; the grader rejects the summary.

Appendix

Table 8: Adaptive and non-adaptive responses to changed log encoding.

	Adaptive behavior
Agent	GPT-5.4-mini, run_00 .
Explanation	Suspects an incorrect program path in the first line or Windows line endings; does not directly inspect the extra character.
Action	Reads the script and runs bash /app/generate_zk_protocols.sh , avoiding the broken first line.
Check	Confirms that the script exists and has execution permission before changing how it is run.
Result	Runs the generator and writes both reports; all 15 tests pass.

Appendix

Table 9: An adaptive response to a broken script launch.

	Adaptive behavior	Non-adaptive behavior
Agent	GPT-OSS-120B, run_00 .	GPT-OSS-120B, run_01 .
Explanation	Finds that a configuration setting prevents pip from searching for packages.	Attributes the remaining failure to Python-version compatibility without confirming that explanation.
Action	Inspects pip configuration and removes no-index from /etc/pip.conf .	Restores pip but leaves its global configuration unchecked.
Check	Successfully installs requests after removing the setting.	Confirms pip’s version but accepts No matching distribution found when testing installation.
Result	Restores package installation; both tests pass.	Leaves installation broken and declares completion. Verifier setup also fails, so the task tests do not run.

Appendix

Table 10: Adaptive and non-adaptive responses to a package-installation restriction.

	Adaptive behavior	Non-adaptive behavior
Agent	GPT-5.4-mini (high), run_01 .	GPT-5.4-mini (high), run_00 .
Explanation	Suspects that an inherited TAR_OPTIONS setting changes the stored names.	Recognizes the wrong names but does not identify the setting that changes them.
Action	Unsets TAR_OPTIONS when recreating and extracting the archive.	Repeats the archive command without changing the setting.
Check	Lists the rebuilt archive and verifies the names app.log and error.log .	Sees the raw/ prefix again but stops after extraction fails.
Result	Extracts the log and completes the outputs; all four tests pass.	Leaves the archive incorrect and the extracted log missing; three tests fail.

Appendix

Table 11: Adaptive and non-adaptive responses to changed archive filenames.

	Adaptive behavior	Non-adaptive behavior
Agent	GPT-OSS-120B, run_00 .	GPT-OSS-120B, run_02 .
Explanation	Identifies that the script treated a repeated header as a release.	Blames a blank line at the end of the CSV for creating an extra file.
Action	Reads the CSV, removes the generated files, and rebuilds the outputs while skipping header lines.	Does not read the CSV. Attempts to delete release-.json , although the extra file is named release-version.json .
Check	Lists the regenerated files and confirms that exactly three remain.	Sees that the count is still four but declares the task complete.
Result	Completes the task; all seven tests pass.	Leaves the extra file and log entry; two tests fail and five pass.

Appendix

Table 12: Adaptive and non-adaptive responses to a repeated CSV header.

	Adaptive behavior	Non-adaptive behavior
Agent	GPT-5.5, run_01 .	GPT-5.5, run_03 .
Explanation	Reads the startup error and identifies an environment setting that requests a missing database script.	Treats a successful build and the expected JAR path as sufficient to declare completion.
Action	Sets the database connection explicitly in the application and rebuilds the JAR.	Configures the database through the properties file, which the environment setting overrides.
Check	Launches the rebuilt JAR, confirms startup, and tests login.	Checks that the JAR exists, but never launches it or tests an endpoint.
Result	Runs the service successfully; all ten tests pass.	The grader cannot start the service; all ten tests report setup errors.

Appendix

Table 13: Adaptive and non-adaptive responses to an overridden database setting.

	Adaptive behavior	Non-adaptive behavior
Agent	Kimi-2.6, run_03 .	Kimi-2.6, run_04 .
Explanation	Recognizes that Git’s saved decision has left the requested biography update missing.	Recognizes that Git kept the old biography, but treats the conflict as resolved.
Action	Retrieves the updated biography from the project history and saves it.	Saves the result with the old biography still in place.
Check	Reads the biography and checks whether it includes the requested Stanford update.	Reads the old biography but accepts Git’s resolved status as sufficient.
Result	Restores the updated biography and page layout; both tests pass.	Restores the layout but leaves the biography unchanged; one test fails and one passes.

Appendix

Table 14: Adaptive and non-adaptive responses to Git reusing an old decision.

Score	Occurrence plausibility	Realization fidelity
1	Implausible or specific to the evaluator	Unfaithful or incoherent implementation
2	Technically possible but contrived	Crude proxy with major artificial artifacts
3	Plausible in some deployments	Controlled abstraction preserving the central effect
4	Recognizable real deployment or configuration change	Faithful simulation with minor simplifications
5	Documented, observed, or widely recognized failure mode supported by the artifacts	Direct or near-exact reproduction of the real mechanism

Appendix

Table 15: The two realism scores are assigned separately using this rubric.

Dataset	Pairs	Counts (1–5)	Mean	$\geq 4$	Counts (1–5)	Mean	$\geq 4$
		Occurrence plausibility			Realization fidelity
ET-eval	140	0, 2, 43, 86, 9	3.73	68%	0, 0, 20, 35, 85	4.46	86%
TB-Lite	118	0, 6, 25, 76, 11	3.78	74%	0, 1, 11, 41, 65	4.44	90%
TB-2	87	0, 2, 18, 60, 7	3.83	77%	0, 0, 12, 29, 46	4.39	86%
All	345	0, 10, 86, 222, 27	3.77	72%	0, 1, 43, 105, 196	4.44	87%

Appendix

Table 16: LLM realism ratings for the final evaluation sets. Counts list scores 1–5;

\geq 4

is the percentage rated 4 or 5, rounded to the nearest integer. Median plausibility and fidelity are 4 and 5, respectively, in every set.

Model	Provider	Model / snapshot	Main effort	Other efforts
GPT-5.5	OpenAI	gpt-5.5 (2026-04-24)	High	–
GPT-5.4-mini	OpenAI	gpt-5.4-mini (2026-03-17)	High	Medium, Low
GPT-OSS-120B	OpenAI	gpt-oss-120B	Default	High, Low
Kimi-2.6	Moonshot	Kimi-K2.6 (2026-04-20)	Default	High, Low, None
DeepSeek-V4-Flash	DeepSeek	DeepSeek-V4-Flash-0731 (2026-07-31)	None	–
Grok-4.20	xAI	grok-4.20-beta-0309	Non-reasoning	–

Appendix

Table 17: Evaluation models. Default means reasoning_effort was omitted (approximately medium for GPT-OSS-120B and Kimi-2.6). Other efforts are used in Table 2 .

Setting	Value
Agent harness	Terminus-2, at most 32 interaction turns.
Rollouts	$K=5$ per model–task pair, unless otherwise specified.
Completion budget	8,192 tokens per model response.
Sampling	Temperature and top- $p$ unset (deployment defaults); no explicit seed.
Verifier timeout	$10\times$ the configured verifier timeout.

Appendix

Table 18: Shared settings for the main evaluation.

	ET-eval				TB-Lite				TB-2
	Interact@1				Interact@1				Interact@1
Model	Base	Novel	Bypass@1	Fail w/o Int.	Base	Novel	Bypass@1	Fail w/o Int.	Base	Novel	Bypass@1	Fail w/o Int.
GPT-5.5	99.1	99.0	0.7	1.2	96.9	96.3	1.7	7.2	96.6	98.3	1.4	1.4
GPT-5.4-mini	95.3	95.9	2.7	3.8	92.7	94.9	1.5	8.6	91.2	95.0	0.7	8.1
Kimi-2.6	97.3	98.4	1.6	0.0	94.2	97.8	0.8	3.3	92.6	96.1	2.3	4.2
DeepSeek-V4-Flash	96.1	97.8	1.3	3.4	95.4	96.6	0.7	5.4	94.6	95.5	0.8	4.2

Appendix

Table 19: Cross-model relevance of curated novelties (%).

Model	Rollouts with $\geq 1$ Retry (%)	$P(\mathrm{Retry}_{t+1}\mid\mathrm{Retry}_{t})$ (%)
	Retry prevalence	Retry persistence
GPT-5.5	9.8	9
GPT-5.4-mini	15.2	50
Kimi-2.6	18.1	6
DeepSeek-V4-Flash	17.4	10
GPT-OSS-120B	27.8	40

Appendix

Table 20: Retry prevalence and persistence on ET-eval novel rollouts (%).

Stage	Definition
Interacted	An executed command touches an affected tool, path, or file. Detected deterministically; no visible effect is required.
Observed	Terminal output exposes novelty-related evidence, such as an error, unexpected result, or changed file state. Recognition is not required.
Recognized	The agent explicitly identifies the observation as unexpected or inconsistent with its assumptions, without necessarily identifying the cause.
Diagnosed	The agent identifies the changed condition sufficiently to explain the problem and guide a response. Restating a symptom is insufficient.
Revised	A changed strategy successfully removes or bypasses the obstacle while preserving the task. Failed attempts and exploits do not count.
Solved	The agent completes the original task under novelty using a task-preserving strategy.

Appendix

Table 21: Stages of novelty handling used in trajectory analysis.

Model	Diag. $\rightarrow$ Rev.	Diag. $\rightarrow$ Retry	Premature Done	Pass $\|$ Diag.	Pass $\|$ Rev.+Diag.	Pass $\|$ Rev. w/o Diag.
GPT-5.5	71	5	14	96	96	68
Kimi-2.6	41	4	11	86	87	50
DeepSeek-V4-Flash	47	5	12	86	86	70
GPT-5.4-mini	57	11	20	87	86	59
GPT-OSS-120B	30	10	21	65	64	22
Grok-4.20	31	14	16	61	64	20

Appendix

Table 22: Detailed behavioral transitions and conditional success rates on ET-eval. Diag.

\rightarrow

Rev. and Diag.

\rightarrow

Retry measure revision or retry immediately after diagnosis. Premature Done denotes trajectories that finalize immediately after a failed or partial step. The final columns report success conditioned on diagnosis and on whether strategy revision occurs with or without a preceding diagnosis. All values are percentages.

Setting	Value used in our experiments
Optimizer	AdamW; $\beta_{1}=0.9$ , $\beta_{2}=0.95$
Weight decay	$0.01$
Learning rate	$10^{-6}$ , constant; no warmup
Gradient clipping	$0.2$
Batch size	$16$
Rollouts per prompt	$16$

Appendix

Table 23: Training hyperparameters used in our experiments, adopted from Shrivastava et al. (2026) . ECHO-specific settings apply only to ECHO runs; run lengths are stated above.

	ET-eval		TB-Lite
Qwen3-14B GRPO	Base	Novel	Base	Novel
Clean	65.3	24.6	17.2	2.9
Novel-paired	67.1	35.5	18.9	5.5

Appendix

Table 24: Qwen3-14B GRPO pass@1 (%) on ET-eval and TB-Lite.

Figure 6: Process rewards change behavior but yield limited gains on novel tasks. Held-out ET-eval, 1,120 novel rollouts per arm. Failed commands are a percentage of calls; Table 3 reports task success.

Figure 7: Qwen3-14B generic post-training on ET-eval (solid) and TB-Lite (dotted). Bars show base and novel pass@1 (%);

\Delta

marks the adaptation gap.

Prompt	Purpose	Usage in Algorithm 1
	Defines novelty for reuse across prompts	Lines 3, 4, 6, 11, 16: shared model context
	Characterizes the base strategy and assumptions	Line 3: $M_{\mathrm{A}}$
	Identifies applicable novelty dimensions	Line 4: $M_{\mathrm{N}}^{\mathrm{dim}}$
	Synthesizes novelty specifications	Line 6: $M_{\mathrm{N}}^{\mathrm{spec}}$
	Instantiates $E^{\prime}_{c}$ and adapted $S^{\prime}_{c}$	Line 11: $M_{\mathrm{N}}^{\mathrm{impl}}$
	Repairs a failed task instantiation	Line 11: $M_{\mathrm{N}}^{\mathrm{impl}}$ retry (implicit)

Appendix

Table 25: Prompt-to-operation mapping for the AGNI pipeline and disclosed-novelty evaluation. Line numbers refer to Algorithm 1 .

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