Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses

Weichen Yu; Lesly Miculicich; Bhavana Dalvi Mishra; Jiefeng Chen; Shuo Li; Matt Fredrikson; Tomas Pfister; Long T. Le

cs.AIOct 10, 2026

Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses

Authors: Weichen Yu, Lesly Miculicich, Bhavana Dalvi Mishra, Jiefeng Chen, Shuo Li, Matt Fredrikson, Tomas Pfister, Long T. Le

Organizations: Google Cloud AI Research · Carnegie Mellon University · Google DeepMind

Abstract

Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback. Existing approaches, however, largely optimize a single objective and evolve the harness as a monolithic artifact, limiting their ability to handle competing goals and to separate transferable design from environment-specific adaptation. We introduce Harmony Harness, a framework for multi-objective, compositional harness evolution. Harmony Harness formulates harness evolution as a multi-objective optimization problem and uses decomposition-based evolutionary search to maintain solutions across different regions of the Pareto frontier, avoiding reliance on a hand-designed structural diversity metric for repository-scale harnesses. In parallel, we introduce factorized evolution operators that separately refine general-purpose harness mechanisms and benchmark-specific adaptations, together with a dynamic composition workflow that integrates them at inference time. Across SecRepoBench and BaxBench, Harmony Harness consistently outperforms strong harness-evolution baselines, with gains exceeding 10% on both benchmarks. Our results show that explicitly modeling both objective trade-offs and harness structure yields substantially stronger and more transferable agent evolution than existing single-objective, monolithic approaches.

Figures & tables

Figure 1: Overview of Harmony Harness . (a) The functionality–security space is split into weighted subproblems. Each subproblem keeps a harness and together they approximate the Pareto frontier. (b) An LLM proposer diagnoses failures and forms a hypothesis. Operators edit tools, skills, or logic, and place each edit in either the general scope or the environment-specific scope. The child is validated before evaluation. (c) At inference, a composition agent picks components from the general and environment-specific pools to build a task-specific harness.

Panel of Figure 2 — Figure 2: Evolution results per split on (a) SecRepoBench and (b) BaxBench.

	SecRepoBench			BaxBench
Method	$\Delta_{\text{evolve}}$	$\Delta_{\text{OUT}}$	retained	$\Delta_{\text{evolve}}$	$\Delta_{\text{OUT}}$	retained
Meta-harness (func-only)	$+3.3$	$-3.4$	$-103\%$	$+3.6$	$-7.1$	$-197\%$
Meta-harness (sec-only)	$+13.3$	$+3.3$	$25\%$	$+17.9$	$+3.6$	$20\%$
Meta-harness (func & sec)	$+16.6$	$+3.3$	$20\%$	$+14.3$	$+3.6$	$25\%$
DGM (func & sec, scalar)	$+20.0$	$+0.0$	${0\%}$	$+10.7$	$+3.6$	$34\%$
Harmony Harness (func & sec)	$+36.6$	$+20.0$	$\mathbf{55\%}$	$+21.4$	$+10.7$	$\mathbf{50\%}$

Table 1: How much evolved security survives a distribution shift.

\Delta_{\text{evolve}}

is a method’s security gain over the un-evolved baseline on the split it evolved on;

\Delta_{\text{OUT}}

is its gain over the same baseline on held-out-OUT; retained

=\Delta_{\text{OUT}}/\Delta_{\text{evolve}}

. Mean over 3 runs.

Strategy	Acceptance rule	Func	Sec	F&S
Static (no evolution)	—	66.7	56.7	46.7
Functionality-only	accept if $F\!\uparrow$	73.3	60.0	56.7
Safety-only	accept if $\text{viol.}\!\downarrow$	70.0	66.7	46.7
Sequential (func $\to$ sec, sec $\to$ func)	one objective per generation, then the other	70.0	76.7	70.0
Prompt-merge variants	merge func & safety prompts	66.7	60.0	50.0
Joint-weighted ( $\alpha\!\in\!\{.25,.5,.75\}$ )	$\alpha F\!+\!(1\!-\!\alpha)S$	70.0	73.3	70.0

Table 2: Evolution strategies on SecRepoBench. Func/Sec/F&S in %. Mean over 3 runs.

Figure 3: Cross-benchmark transfer. Each cell is one (optimized-on $\times$ evaluated-on) pair, split into meta-harness (left) and Harmony Harness (right); dashed outlines mark in-domain cells, solid outlines zero-shot transfer.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Symbol	Meaning
Problem formulation (§ 3 )
$\mathcal{H}$ , $H$	Space of agent harnesses; a harness
$\mathcal{D}=\{t_{n}\}_{n=1}^{M}$	Evaluation task set of $M$ tasks
$t_{n}=(x_{n},p_{n})$	Task $n$ : task specification $x_{n}$ and security policy $p_{n}$
$\tau\sim P_{H}(\tau\mid x_{n})$	Trajectory of harness $H$ on task $t_{n}$ (conditioned on the visible spec $x_{n}$ )
$F(\tau,x_{n})\in\{0,1\}$	Functional success: the solution passes the tests of $x_{n}$

Appendix

Table 3: Summary of notation.

Operator	Writable directories	Required deliverable
tool	tools/ , utils/ (plus config/ )	A change to how the agent obtains or consumes ground truth , covering one of four cases: no real tool is wired into the loop; a tool is called but its result is wasted (the log is ignored, or only the build is checked and never the server’s startup); the tool itself returns too little to act on; or a helper in utils/ discards the critical information before it reaches the agent. Phantom verification is explicitly forbidden.
skill	skills/ , prompts/	One reusable skill: a trigger condition, a short general procedure, and one worked example. The skill is a single .py module dropped into the scope directory, with no registration needed. Skills written as a default behavior with an escape clause (e.g. “unless the spec says otherwise, return 200”) are forbidden. In one of our runs, such a skill fixed its target task but broke two unrelated ones.
logic	steps/ , orchestrator/ ; at general scope, also the loop modules at the package root	A change to the control flow : add a missing stage, act on a branch that is currently ignored, adjust round counts and budgets (the values go in config/ ), or change the DAG itself. Achieving the same effect by editing prompts is not allowed.

Appendix

Table 4: The three scoped variation operators. Each operator may write only its own directories, at the scope chosen before the edit; any write outside them is reverted.

Strategy	Acceptance rule	Func	Sec	F&S
Joint Evolving	Tchebycheff over $(F,S)$	70.0	76.7	63.3
+ factored evolving	+ factorized operators (§ 4.2 )	70.0	83.3	66.7
+ dynamic orchestration	+ dynamic composition (§ 4.3 )	76.7	93.3	76.7

Appendix

Table 5: Component ablation of joint evolution on SecRepoBench (search split, %). Each row adds one component to the row above. The bottom row (+ dynamic orchestration) is the full method and is repeated from Table 9 (mean over 3 runs).

Orch.	Operator pool	Seed	Gen-3 children	Best
✓	tool , skill , logic	66.7	83.3, 83.3, 33.3	83.3
✓	+ generalize	66.7	100.0, 50.0, $\dagger$	100.0
✗	tool , skill , logic	33.3	66.7, 66.7, 66.7	66.7
✗	+ generalize	33.3	66.7, 83.3, 66.7	83.3

Appendix

Table 6: Operator-pool ablation (F&S, %). “Gen-3 children” lists the F&S of each child produced in the third generation; “Best” is the best F&S reached by the run.

\dagger

: run that leads to fallback.

Figure 5: Search-split Pareto front of the MOEA/D-alone run (Table 13 ): the baseline and all

15

children, shaded by generation. After generations 1–2 the front holds two non-dominated harnesses (dashed); in generation 3, moead_g003_s01 reaches the ideal point

z^{\ast}

and dominates the rest (solid). Children with identical scores are offset horizontally so that each stays visible. An interactive version is available at https://anonymous.4open.science/w/HH_visualization-4F69/index.html .

Figure 6: Search-set Pareto front of a MOEA/D run on BaxBench (

N=6

subproblems, 28-task cover set, generations 1–4): the two seed harnesses and all

24

children, shaded by generation. After generation 1 the front is a single harness (dashed); from generation 2 on it holds two mutually non-dominated harnesses (solid). Children with identical scores are offset horizontally so that each stays visible. An interactive version is available at https://anonymous.4open.science/w/HH_visualization-4F69/baxbench.html .

Evolution surface	Held-out-IN	Held-out-OUT	comb.
Monolithic summarize $\to$ rewrite	40.0	30.0	35.0
Factored: evolving tools only	46.7	33.3	40.0
Factored: skills/logic only	50.0	46.7	48.3
Factored: tools $+$ skills/logic	60.0	56.7	58.3
$+$ dynamic orchestration (full)	86.7	66.7	76.7

Appendix

Table 7: Evolution-surface ablation (held-out combined F&S, %). Same objective, budget, and seed; only what is evolved changes. “comb.” is the unweighted mean of the two 30-task splits. The full row is repeated from Table 9 (mean over 3 runs).

Execution	Nodes	Sec	Func	F&S	Avg. nodes	Avg. tokens
Baseline (no evolution)	—	50.0	60.7	42.9	—	31,902
Fixed linear pipeline (orchestrator off)	5	53.6	71.4	53.6	5.0	58,230
Dynamic workflow (orchestrator on)	5–8	64.3	75.0	64.3	6.2	63,064

Appendix

Table 8: Dynamic orchestration ablation on BaxBench ( USE_ORCHESTRATOR on/off). The lower two rows are the same evolved harness, seed, and budget, differing only in the toggle; the un-evolved baseline is repeated from Table 10 for reference. “Nodes” is the range of plan sizes observed over the 28 tasks (Figure 8 ; the planner may emit up to 16), “avg. nodes” the mean number actually executed, and “avg. tokens” the mean solve-time token spend per task.

Method	Search			Held-out In			Held-out Out			Avg. F&S
Method	Sec	Func	F&S	Sec	Func	F&S	Sec	Func	F&S	Avg. F&S
Baseline	56.7	66.7	46.7	66.7	73.3	50.0	46.7	70.0	36.7	44.4
Meta-harness (func & sec)	73.3	70.0	53.3	70.0	73.3	56.7	50.0	70.0	43.3	51.1
Meta-harness (func-only)	60.0	73.3	56.7	60.0	73.3	50.0	43.3	70.0	36.7	47.8
Meta-harness (sec-only)	70.0	66.7	46.7	66.7	60.0	53.3	50.0	63.3	33.3	44.4
DGM (func & sec, scalar)	76.7	73.3	60.0	66.7	70.0	53.3	46.7	66.7	33.3	48.9

Appendix

Table 9: Evolution on SecRepoBench (%). Best result per column in bold . Mean over 3 runs.

	Search			Held-out IN			Held-out OUT			Avg.
Method	Sec	Func	F&S	Sec	Func	F&S	Sec	Func	F&S	F&S
Baseline (no evolution)	50.0	60.7	42.9	50.0	62.4	42.9	46.4	57.1	42.9	42.9
Meta-harness (func & sec)	64.3	71.4	60.7	57.1	62.4	53.6	50.0	60.7	42.9	52.4
Meta-harness (func-only)	53.6	71.4	46.4	53.6	64.3	50.0	39.3	60.7	39.3	45.2
Meta-harness (sec-only)	67.9	50.0	46.4	60.7	57.1	53.6	50.0	53.6	42.9	47.6
DGM (func & sec, scalar)	60.7	71.4	57.1	50.0	64.3	50.0	50.0	57.1	39.3	48.8

Appendix

Table 10: Evolution on BaxBench (%, secure web-app generation). Same protocol as Table 9 . “Average” is the mean F&S over the three splits. Mean over 3 runs.

Language	$n$	One-shot (cc)			$+$ HH harness (ours)
Language	$n$	Sec	Func	F&S	Sec	Func	F&S
C	93	75.3	88.2	75.3	89.2	90.3	89.2
C++	63	76.2	98.4	76.2	90.5	93.7	90.5
Go	57	70.2	87.7	70.2	89.5	89.5	89.5
JavaScript	69	72.5	89.9	72.5	87.0	87.0	82.6
Python	75	74.7	96.0	70.7	90.7	89.3	85.3

Appendix

Table 11: HH evolved on BaxBench , transferred zero-shot to CWEval (%), against the un-evolved one-shot model with Claude Code harness (cc) on the same tasks. Better of the two harnesses per metric and per language in bold . Mean over 3 runs.

Method	Evolved on	Evaluated on	Sec	Func	F&S
Baseline (no evolution)	—	BaxBench	50.0	60.7	42.9
Meta-harness	SecRepoBench	BaxBench	53.6	57.1	39.3
HH (ours)	SecRepoBench	BaxBench	59.6	64.3	57.1
Baseline (no evolution)	—	SecRepoBench	56.7	66.7	46.7
Meta-harness	BaxBench	SecRepoBench	43.3	50.0	36.7
HH (ours)	BaxBench	SecRepoBench	80.0	70.0	70.0

Appendix

Table 12: Cross-benchmark transfer (%). A harness is evolved on one benchmark and evaluated on the other with no further evolution, on the target’s search split (the split whose baseline appears in Tables 9 and 10 ); the baseline row is the un-evolved harness on that split. Mean over 3 runs.

stage	hypervolume	$z^{\ast}$ (func, sec)	most-balanced harness
init (baseline)	0.321	(56.7, 56.7)	baseline (56.7, 56.7)
generation 1	0.528	(70.0, 76.7)	moead_g001_s00 (70.0, 70.0)
generation 2	0.528	(70.0, 76.7)	moead_g001_s00 (70.0, 70.0)
generation 3	0.537	(70.0, 76.7)	moead_g003_s01 (70.0, 76.7)

Appendix

Table 13: Per-generation frontier quality for MOEA/D alone (

N=5

T=2

, 3 generations; no factored evolving or dynamic orchestration, to isolate the decomposition) on the SecRepoBench search set. Hypervolume is the area of the population’s Pareto front against reference point

(0,0)

;

z^{\ast}

is the ideal point (per-objective running best); “most-balanced” is the population member with the highest worst objective (

\max\min(\text{func},\text{sec})

	Held-out IN (%)			Held-out OUT (%)			comb.
harness	Func	Sec	F&S	Func	Sec	F&S	F&S
baseline	73.3	66.7	50.0	70.0	46.7	36.7	43.3
without env-feedback	—	—	40.0	—	—	30.0	35.0
feedback-env gen1 best	63.3	80.0	56.7	66.7	56.7	43.3	50.0
feedback-env gen2 best	70.0	80.0	60.0	80.0	63.3	56.7	58.3
feedback-env gen3 best	80.0	83.3	66.7	76.7	50.0	46.7	56.7

Appendix

Table 14: Held-out generalization (30 tasks each, eval-only). IN

=

crash-type families seen during search; OUT

=

unseen families. The feedback-tool per-generation bests beat baseline on combined F&S (

58.3

43.3

); the search-seeded winner is worse than baseline (

35.0

Figure 7: Population diversity over

137

evolved BaxBench harnesses (

34

run tags). Left: the five components and their equal-weight composite (

\mathrm{DIVERSITY}=0.300

, dashed). The index is held up by mean pairwise distance

D_{\text{struct}}

and label entropy

H_{\text{label}}

and held down by nearest-neighbour distance

D_{\text{novel}}

and the fraction of distinct solutions

N_{\text{eff}}/n

. Middle:

N_{\text{eff}}

against the duplicate threshold

\epsilon_{\mathrm{dup}}

;

N_{\text{eff}}

falls steeply (

112

classes at

\epsilon_{\mathrm{dup}}=0.001

down to

9

\epsilon_{\mathrm{dup}}=0.05

) and flattens only briefly (still

9

0.1

), so “how many distinct solutions” has no threshold-free answer and the reported

9

describes the scale of a typical edit. Right: the distance distributions behind the headline—over all

\binom{137}{2}

pairs the mass sits far from zero (top), while over each genome’s nearest neighbour it concentrates in the first bin (bottom, note the change of vertical scale).

Quantity	Value	Reading
$d_{\text{tok}}$ / $d_{\text{ast}}$ / $d_{\text{lin}}$	$0.417$ / $0.162$ / $0.433$	AST distance is $2.6\times$ smaller than token and line distance: edits move text and individual statements, rarely control structure
$D_{\text{struct}}$	$0.345\pm 0.269$	globally the population is well spread
$D_{\text{novel}}$	$0.014$	… but nearest neighbours are close in code
$N_{\text{eff}}$ / $n$	$9/137=6.6\%$	nine equivalence classes at $\epsilon_{\mathrm{dup}}=0.05$
$N_{\text{eff}}$ sweep	$0.001{\to}112$ $0.005{\to}68$ $0.01{\to}30$ $0.05{\to}9$ $0.1{\to}9$ $0.2{\to}7$
$H_{\text{label}}$	$0.934$	134/137 labelled, 17/17 labels hit, $\mathrm{Simpson}=0.914$

Appendix

Table 15: Pooled population,

n=137

variants from 34 run tags,

g_{\mathrm{sh}}=5

, weights

(0.4,0.3,0.3)

\epsilon_{\mathrm{dup}}=0.05

Label	Frac.	Label	Frac.	Label	Frac.
build-verify	$0.78$	skill	$0.53$	parsing	$0.30$
planning	$0.70$	config-knob	$0.45$	self-critique	$0.25$
security	$0.64$	tool-add	$0.41$	error-handling	$0.14$
loop-control	$0.62$	test-gen	$0.40$	context-mgmt	$0.12$
prompt-edit	$0.61$	retry	$0.36$	edit-format	$0.08$
		input-validation	$0.30$	model-params	$0.01$

Appendix

Table 16: Mechanism-label distribution over the 134 labelled children. Labels are not exclusive; the fraction is the share of children carrying that label.

Flag	Default	Effect
--ngram	$5$	token shingle length $g_{\mathrm{sh}}$
--weights	0.4,0.3,0.3	token, AST, line weights; renormalized
--dup-threshold	$0.05$	$\epsilon_{\mathrm{dup}}$ for $N_{\text{eff}}$ and the near-duplicate list
--include-seeds	off	add bax_repo_seed / bax_wf_seed
--top	$5$	length of the most-novel and closest-pair lists
--json	—	write every metric, including the per-genome nearest-neighbour distances, the near-duplicate pairs and the per-tag breakdown

Appendix

Table 17: Options.

ID	Mechanism fitted	Injected edit
Tier 1 — lexical
P1	task identity	if task_id in {...} guarding a bespoke code path, with evolve-split IDs enumerated
P2	crash-type string	a crash-type name drawn from the evolve split hardcoded into the prompt template
P3	repository name	if "libpng" in repo_path selecting a hand-written fix recipe
P4	task text	a skill file whose body names specific evolve-split tasks
Tier 2 — structural

Appendix

Table 18: The planted-overfit taxonomy. Each plant is injected into a real admitted child as the sole modification. Tier 1–2 labels are certain by construction; Tier 3 and class S labels are verified behaviourally (§ K.4 ).

Figure 8: Workflow DAGs planned by the evolved orchestrator over

28

BaxBench tasks. Solid edges are unconditional; dashed nodes/edges fire only under a when guard. Plans range from

5

8

nodes and are re-planned per task, rather than fixed at harness-design time.

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