Capability Engineering: Models and Applications

• Capability Engineering is a discipline that systematically specifies and quantifies system abilities based on task-specific, contextual constraints using formal models and semantic policies.
• It employs structured protocols like the CAPE loop for specification, verification, and correction, achieving an 81% reduction in error rates and significant cost savings.
• The methodology supports diverse applications—from autonomous systems and manufacturing to human–robot collaboration—by translating contextual requirements into machine-executable skills.

Capability Engineering is a discipline focused on the systematic specification, measurement, and realization of system abilities—“capabilities”—in response to explicit requirements. Unlike generic competence or intelligence measures, capabilities are defined with respect to task- or domain-specific, context-dependent constraints and mapped directly to system behaviors, performance metrics, or interfaces. Capability engineering encompasses formal modeling, verification, and operationalization of these capabilities, spanning domains including machine learning, autonomous systems, manufacturing, and human–robot collaboration. This article synthesizes frameworks, methodologies, and empirical advances underpinning the field.

1. Formalisms and Taxonomies for Capabilities

Capabilities are commonly modeled as explicit, fine-grained specifications describing system behaviors, performance, or interface affordances. In machine learning, a capability $C$ is formalized as $(S_C, E_C)$, where $S_C \subseteq X$ is a subset of the input space exercising the behavior and $E_C: S_C \to Y$ defines the expected system response, such as the ground-truth label (Yang et al., 2022). In cyber-physical systems and robotics, capabilities are described as machine-interpretable modules or “skills” exposing parameterized preconditions and effects over typed resources or properties (Köcher et al., 2022, Köcher et al., 2023). For human–robot teams, capabilities are treated as coordinated vectors in a capability space $\mathbf{Q}^n$, measuring joint or residual ability across task-relevant axes (Mandischer et al., 2024).

Capability abstractions subsume various evaluation and engineering constructs:

Context	Capability abstraction example	Specification modality
ML model	“handles negation in sentiment analysis”	Dataset slicing, soft metrics
Robotics	“can transport object to coordinate X”	Semantic model, constraints
Human–robot team	“team can reach, grasp, and position”	Capability deltas, optimization
AI assistant	“respects regulatory drug formulary”	Executable policy language

This formalism enables unification across safety, generalization, fairness, and process-planning tasks (Yang et al., 2022, Ball, 15 Dec 2025, Köcher et al., 2023).

2. Specification, Verification, and Correction Protocols

Capability engineering mandates the explicit translation of contextual requirements into executable or checkable artifacts. In modern AI, this is instantiated by the CAPE protocol (Capability Achievement via Policy Execution), which comprises a closed Specify → Verify → Correct → Train loop:

• Specification: Requirements are authored as DSLs (e.g., CPL—CAPE Policy Language), semantic policies, or test slices. For structural properties, symbolic predicate graphs are extracted from outputs and verified for compliance (Ball, 15 Dec 2025).
• Verification: Structural checks are deterministic (e.g., "tool_call.arguments.value == last(operations).output"), while semantic properties rely on learned verifiers trained against context-resolved rubrics. Verification accuracy scales positively with model scale (Pearson $r = 0.94$) (Ball, 15 Dec 2025).
• Correction: Violations are programmatically patched, templated, or minimally re-generated. Empirically, deterministic corrections succeed in 99.7% of structural cases; template and LLM-based fixes achieve 97.3% and 94.6% respectively.
• Model Internalization: Models are retrained on corrected samples, directly internalizing format or policy adherence (Ball, 15 Dec 2025).

This protocol yields an 81% reduction in violation rates over preference-optimization (DPO), with per-example annotation costs reduced by 5–20×.

3. Capability Modeling in Planning, Robotics, and Industrial Automation

In process planning and robotics, capabilities are formalized as functionally annotated modules within ontological frameworks (e.g., CaSk, OWL-based), enabling machine-readable preconditions, effects, and constraints (Köcher et al., 2023). Automated planners transform capability models into Satisfiability Modulo Theory (SMT) problems:

• Encoding: Capabilities $c\in C$ define input set In$(c)$, output set Out$(c)$, and logical constraints (pre/post-conditions, effects).
• Planning: Given a required capability and current state, SMT solvers (e.g., Z3) search for execution sequences such that post-execution state satisfies goal requirements, handling arithmetic constraints and property synonyms natively.
• Human-in-the-Loop: Expert constraints, explanations (via unsatisfiable cores), and trace generation for process documentation are supported by mapping logical variables back to ontology IRIs (Köcher et al., 2023).

In modular cyber-physical production systems (CPPS), capability engineering is realized using model-driven engineering (MDE) methods:

• Abstraction: Functions (solution-neutral verb-noun system behaviors) are decomposed into roles and implemented as machine-executable skills (annotated classes with state machines and semantic metadata).
• Transformation: Metamodels (SysML/UML) are mapped to runtime artifacts (Java skill classes, BPMN processes); cross-role communication is orchestrated via typed ports and signals.
• Reconfiguration: Roles and skills decouple high-level functionality from concrete module realization, supporting compositional reconfiguration and dynamic process chaining (Köcher et al., 2022).

4. Measurement and Optimization of Capabilities

Capabilities are measured quantitatively, supporting diagnostic, assurance, and optimization workflows:

• Failure rate on slices: For ML models, the capability failure rate on a slice $D_C$ is defined as $$\phi_C(M) = \frac{1}{|D_C|}\sum_{(x,y)\in D_C} \mathbf{1}\{M(x) \neq y\}$$, with thresholds $\tau_C$ or accuracy $\alpha_C$ serving as operational bounds.
• Capability deltas: In adaptive human–robot teaming, the capability-delta in task $k$ is $$\Delta \mathbf{c}^{(k)} = \mathbf{r}^{(k)} - \mathbf{c}^T$$, measuring under- or over-fulfilment per axis. Compensation behaviors are computed by solving $a^{\star}_j = \min\{c^A_j, \max\{0, \delta_j\}\}$ for each axis $j$ (Mandischer et al., 2024).
• Empirical generalization: Capability-slice accuracy is predictive of out-of-distribution generalization; in a WILDS benchmark experiment, inclusion of $s_{C_j}(M)$ features led to statistically significant improvements in $R^2$ explaining generalization to novel domains (Yang et al., 2022).
• Adherence profiles: Public registries (e.g., CapabilityBench) report capability adherence as a vector across policy suites, including violation distribution and optional/core policy compliance (Ball, 15 Dec 2025).

5. Application Scenarios: ML Engineering, Multimodal AI, Human–Robot Teams, and Automation

Capability engineering serves diverse domains:

• ML model lifecycle: At design, debugging, evaluation, and maintenance phases, capabilities are used for requirements specification, error grouping, coverage-driven retraining, and regression testing. Capabilities provide a unified abstraction for robustness, fairness, and behavioral conformance (Yang et al., 2022).
• Multilingual/multimodal AI: MRRE (Multilingual Reasoning via Representation Engineering) steers model internal states via vector injection at inference time, unlocking capabilities (e.g., multilingual reasoning) without retraining. This paradigm extends to bias mitigation, safety alignment, and other latent capability gaps (Li et al., 28 Nov 2025).
• Manufacturing process planning: Automated planning composes vendor-independent capabilities into production plans using SMT encoding, eliminating manual PDDL modeling and supporting rapid integration of heterogeneous resources (Köcher et al., 2023).
• Human–robot collaboration: Capability deltas provide a design and runtime metric for distributing control and assistance in teams, enabling adaptive support tuned to ergonomic, safety, or efficiency objectives (Mandischer et al., 2024).
• Service-oriented automation systems: Modeling functions and skills supports runtime flexibility, rapid reconfiguration, and traceability from requirements to code in CPS and industrial IoT settings (Köcher et al., 2022).

6. Challenges and Future Directions

Key research challenges include:

• Identification and abstraction: Automating capability discovery and developing re-usable capability taxonomies tailored for domains with complex, evolving tasks (Yang et al., 2022).
• Verification at scale: Managing extraction errors, semantic verifier hallucinations, rubric ambiguity, and ongoing policy drift in systems with dynamic requirements (Ball, 15 Dec 2025).
• Integration and extensibility: Embedding capability engineering into existing toolchains (MLOps, CAM, BPMN orchestration), extending support to durative actions, concurrent processes, and open-ended dialog scenarios (Köcher et al., 2022, Köcher et al., 2023, Ball, 15 Dec 2025).
• Human-aligned communication: Building terminology and interfaces for multi-stakeholder capability negotiation and conflict resolution (Yang et al., 2022).
• Normative boundaries: Quantifying the relationship between capability specification granularity, generalizability, and risk; studying tradeoffs in levels of abstraction (Yang et al., 2022).

A plausible implication is that capability engineering, via specification-driven development and verification, is poised to become foundational in the engineering of trustworthy, adaptive, and extensible AI and automation systems. The shift from aggregate “intelligence” benchmarking toward multidimensional, context-conscious capability evaluation underpins this evolution across technical domains (Ball, 15 Dec 2025, Yang et al., 2022, Köcher et al., 2023, Li et al., 28 Nov 2025).