Semi-Autonomous Agents: Design & Evaluation

• Semi-autonomous agents are computational systems that perform complex tasks with significant autonomy while enabling human oversight at critical decision points.
• Their architectures combine modular design, neuro-symbolic reasoning, and dynamic policy hierarchies to balance efficiency, scalability, and safety.
• Empirical evaluations reveal high diagnostic accuracy, reduced human intervention, and robust performance benchmarks, underscoring their practical impact.

Semi-autonomous agents are computational systems that operate with a significant degree of autonomy in the execution of complex tasks, while retaining explicit mechanisms for human oversight, constraint, or intervention at critical junctures. These agents are prevalent in high-stakes environments, dynamic multi-agent systems, and human–AI collaborative settings where the balance between operational efficiency, robustness, and trustworthiness necessitates a nuanced, context-dependent autonomy model. Recent advancements in LLMs, neuro-symbolic architectures, and multi-agent frameworks have catalyzed the emergence of semi-autonomous agents with fine-grained control modalities, structured reasoning, and verifiable behavioral constraints.

1. Formal Definitions and Levels of Autonomy

The concept of semi-autonomy is typically treated within a spectrum of agent autonomy levels, often indexed as $L\in\{1,\ldots,5\}$: (1) Operator, (2) Collaborator, (3) Consultant, (4) Approver, (5) Observer. Semi-autonomous agents predominantly operate at intermediate levels (L2, L3), facilitating mixed-initiative or consultative workflows in which agents and humans alternately plan, delegate, provide feedback, and validate system behavior. Each level affords explicit user intervention mechanisms, ranging from shared plan editors and “take over” controls (L2) to feedback channels and revision requests (L3) (Feng et al., 14 Jun 2025).

A competence-aware formalism further models the agent’s proficiency at each autonomy level via a state-augmented Markov Decision Process (MDP), allowing dynamic selection of the optimal autonomy degree given state–action–feedback history (Basich et al., 2020). The aggregation of autonomy, competence, and intervention signals is operationalized by constraining the policy set, state representation, and transition dynamics of the agent.

2. Architectural and Algorithmic Patterns

Semi-autonomous agent architectures universally incorporate modular decomposition, explicit state hierarchies, and human-in-the-loop (HITL) interfaces. Representative blueprints include:

• Neuro-Symbolic Multi-Agent Systems: Agents maintain belief states as Kripke models $M=(W,R,V)$, where $W$ represents possible worlds, $R$ denotes accessibility relations, and $V$ encodes valuations of atomic propositions. Modal logic (necessity $\Box p$, possibility $\Diamond p$) enables filtering hypotheses against domain-invariant constraints. LLMs (LMs) serve as hypothesis generators, bounded by modal axioms enforced through symbolic modules, and all updates are audited for consistency and verifiability (Sulc et al., 15 Sep 2025).
• Step-Level Policy Autonomy: Fine-grained execution is achieved by making “Step” the atomic decision unit; agents dynamically select among skills and tool-calls per-step, subject to a four-tier Task/Stage/Agent/Step hierarchy. This hybridizes local policy dynamism with top-down structure for efficient oversight and traceability (Zhou et al., 15 Aug 2025).
• Collaborative Multi-Agent Pipelines: Systems such as BMW Agents orchestrate complex workflows through explicit planning (via LLM-driven decomposition to DAGs), execution (modular agent units), verification, and knowledge sharing. Both intentional and incidental human oversight are supported, and episodic memory is leveraged for cross-task retrieval and fault recovery (Crawford et al., 2024, Tariq et al., 24 Oct 2025).

These architectures feature clear separation between planning, execution, and verification layers, modular role assignment, and audit trails for state transitions and outputs.

3. Knowledge Representation, Reasoning, and Control

Semi-autonomous agents employ an explicit, modular separation between data-driven inductive reasoning and deductive constraint satisfaction:

• Immutable Domain Knowledge and Logical Constraints: Immutable (domain-specific) knowledge is encoded as global logical axioms. Modal formulas such as $\Box(p \rightarrow q)$ (causal direction), $\Box\lnot(p \wedge q)$ (mutual exclusion), and $\Box(p \rightarrow \lnot q)$ (pruning spurious cause) prune unphysical or unsafe actions by LMs before belief state updates (Sulc et al., 15 Sep 2025).
• Retrieval-Augmented and Symbolic Verification: Agents query retrievable knowledge bases aligned with external standards (e.g., MITRE ATT&CK, bibliographic databases) for evidence collection and validation. Summarization, artifact extraction, and feedback cycles update local and global task trees, with structured loops for iterative refinement and error handling (Kobayashi et al., 21 Feb 2025, Tariq et al., 24 Oct 2025).
• Partial Observability and Aspect Separation: Aspective agent systems restrict agent views to controlled information projections $\alpha_k(S)$ to formalize and prove zero information leakage under strong adversarial prompting, while allowing safe, localized environment updates via asynchronous event-driven loops (Bentley et al., 3 Sep 2025).

4. Human–Agent Interaction and Oversight

Central to semi-autonomous agents is the fine-tuned interface through which humans intervene, direct, or override agent behavior:

• Explicit Human Checkpoints & Approval Gates: Critical execution steps (e.g., root-level exploits, data deletion, acceptance of generated content) are gated by human approval, presented alongside agent rationale and plan state (Kobayashi et al., 21 Feb 2025, Tariq et al., 24 Oct 2025).
• Feedback Elicitation and Mixed-Initiative Control: Architecture supports consultation interrupts and revision requests, with structured or free-form communication interfaces for human feedback (Feng et al., 14 Jun 2025). Human interventions may be mandated after repeated failed refinements or at task boundaries.
• Auditability and Provenance: All state transitions, revisions, artifact generations, and consent flows are logged with cryptographic hashes and unique IDs to support transparent post-hoc audit and accountability (Tariq et al., 24 Oct 2025).
• Learning and Adaptation of Autonomy Level: Competence-aware agents employ online Bayesian estimation of human feedback profiles and adapt autonomy levels using value function optimization over the human–agent cost tradeoff (Basich et al., 2020).

5. Evaluation Metrics, Empirical Findings, and Limitations

Rigorous evaluation of semi-autonomous agents relies on empirical metrics reflecting accuracy, consistency, efficiency, and safety:

Metric	Definition/Source	Representative Result
Diagnostic accuracy	Fraction of correct diagnoses	100% (cascade failure scenario) vs. 65% (LM-only baseline) (Sulc et al., 15 Sep 2025)
Logical consistency	Symbolic axiom violation rate	Zero detected violations across all runs (Sulc et al., 15 Sep 2025)
Human effort reduction	Manual steps/hours saved	80% reduction in manual steps, 30% wall-clock saving (Kobayashi et al., 21 Feb 2025)
Factuality	$$F_\mathrm{factual}=1-N_{\mathrm{hallu}}/N_{\mathrm{claims}}$$	$F_\mathrm{factual}\approx0.92$, 8% detected hallucinations (Tariq et al., 24 Oct 2025)
Information leakage	% runs with unauthorized data leakage	0% in aspective agents vs. 83% in baseline (Bentley et al., 3 Sep 2025)
Throughput	Accepted/generated output ratio	0.5 for HIKMA conference pipeline (Tariq et al., 24 Oct 2025)

Empirically, semi-autonomous architectures yield high trustworthiness, verifiability, and resilience. Key limitations include residual risks from indirect prompt-injection, bottlenecks due to frequent human–agent handoff, limited scalability in dense inter-agent communication, and open theoretical questions regarding the training and convergence properties of dynamic policy hierarchies (Bentley et al., 3 Sep 2025, Zhou et al., 15 Aug 2025).

6. Generalization and Domains of Application

The semi-autonomous paradigm is generalizable across technical domains demanding both automation and regulatory or safety controls:

• Scientific Conferences: HIKMA demonstrates end-to-end AI curation, drafting, peer review, and archival, governed by license/IP parsing, rubric-based reviewer agents, and human chair overrides (Tariq et al., 24 Oct 2025).
• Penetration Testing and Security: Multi-LLM modular agents formalize attack trees, command execution, and summarization with explicit approval gates (Kobayashi et al., 21 Feb 2025).
• Industrial Diagnostics: Modal logic-constrained agents achieve robust, explainable root-cause analysis in simulated particle accelerator environments (Sulc et al., 15 Sep 2025).
• Process Automation and Collaboration: Orchestrated multi-agent frameworks (BMW, Allen) generalize to RPA, code generation, document workflows, and any chained API-calling pipelines under HITL supervision (Crawford et al., 2024, Zhou et al., 15 Aug 2025).

7. Open Research Challenges and Future Directions

Key open research issues include formalizing and certifying levels of autonomy, designing adaptive consultation and delegation algorithms, scaling coalition and negotiation protocols in dynamic, context-aware multi-agent systems, and ensuring robust human–AI alignment in the face of evolving societal norms and emergent collective behaviors (Li et al., 5 Feb 2025, Feng et al., 14 Jun 2025). The development of autonomy certificates, quantitative autonomy benchmarks, and provable safety/convergence guarantees constitute foundational directions for advancing the reliability and adoption of semi-autonomous agents in critical systems.