Agentic Gap Analysis in AI Systems

Updated 7 June 2026

Agentic gap analysis is the study of discrepancies in safety, performance, and interpretability between isolated models and their dynamic, context-aware agentic deployments.
It employs frameworks like AgentSeer to decompose executions into action and component graphs, enabling systematic tracing of vulnerabilities and quantification of attack success rates.
Empirical findings show significant risk upticks in agentic deployments, underscoring the need for continuous evaluation and specialized safeguards in multi-step, tool-integrated systems.

The agentic gap, as rigorously defined in the literature, is the disparity in safety, performance, or interpretability profiles between models evaluated in isolation ("model-level") and those assessed as deployed agentic systems with tool integration, multi-step execution, persistent memory, and inter-agent coordination. This systemic delta arises because traditional evaluation frameworks and governance protocols largely probe single-turn, contextless text generation, failing to capture context-dependent, emergent vulnerabilities and failure modes characteristic of agentic deployments (Wicaksono et al., 5 Sep 2025).

1. Formal Definition and Conceptual Scope

The agentic gap is formally characterized as the observable difference in a model's behavior, capability, or risk exposure when running as a stateless model compared to its operation within a stateful, tool-augmented, agentic orchestration. For safety, the gap is measured as the delta in attack success rate (ASR) or other relevant risk metrics between model-level and agentic-level evaluation:

$\mathrm{Agentic\ Gap} = \mathrm{ASR_{agentic}} - \mathrm{ASR_{model}}$

where ASR is the Attack Success Rate as defined by

$\mathrm{ASR} = \frac{\#\{\mathrm{successful\ attacks}\}}{\#\{\mathrm{total\ attempts}\}} \times 100\%$

Agentic gap analysis further generalizes to any systematic discrepancy in observed properties (accuracy, safety, robustness, interpretability, accountability) as a system transitions from a controlled, static evaluation to dynamic, agentic deployment with external tool calls, memory, or multi-agent flows (Wicaksono et al., 5 Sep 2025, Fa et al., 23 Apr 2026, Khan et al., 23 Jun 2025).

2. Motivating Factors and Failure Modes

The critical sources of the agentic gap include:

Limited context in model-level tests: Stateless prompt–response evaluation omits memory, tool outputs, and multi-step state, thereby missing vulnerabilities that only occur when agents maintain long histories or execute tool-mediated plans.
Emergent agentic-only vulnerabilities: New attack vectors—malicious prompt injection through memory, tool orchestration errors, or lateral movement via agent-transfer—manifest exclusively in agentic operation and are not detectable via standard LLM red-teaming (Wicaksono et al., 5 Sep 2025, Wicaksono et al., 21 Sep 2025).
Semantic over syntactic exploits: Agentic systems are vulnerable to context-dependent, semantically-crafted adversarial manipulations (e.g., stepwise reenactment, memory poisoning), which bypass brute-force input engineering and are uncorrelated with prompt length or format (Wicaksono et al., 5 Sep 2025).
Propagation and amplification effects: Errors and vulnerabilities can cascade across tool invocations, memory updates, and multi-agent hops, further broadening the gap as compared to local, single-step failures.

These points underscore that agentic gap analysis is not merely a diagnostic for surface-level discrepancies but probes the deep, systemic divergences that arise with deployment-level complexity (Wicaksono et al., 5 Sep 2025, Wicaksono et al., 21 Sep 2025, Fa et al., 23 Apr 2026).

3. Methodological Frameworks for Agentic Gap Analysis

Action and Component Graph Decomposition (AgentSeer)

Agentic gap analysis leverages fine-grained observability frameworks, most notably the AgentSeer system, which decomposes agentic executions into two primary graph abstractions:

Action graph $G_A = (V_A, E_A)$ : Nodes represent atomic operations—LLM calls, tool invocations, inter-agent communication; edges encode temporal ("happens-before") or dataflow relationships.
Component graph $G_C = (V_C, E_C)$ : Nodes represent structural elements—agents, tools, short- and long-term memory; edges represent operational connections (e.g., tool usage, memory reads/writes).

A merged knowledge-graph representation annotates each action node with input/output tokens, agent identity, and component labels, enabling systematic tracing and red-teaming across execution pathways (Wicaksono et al., 5 Sep 2025).

Evaluation Protocols

Agentic gap identification requires paired evaluations:

Model-level: Traditional single-prompt, stateless iterative refinement (e.g., PAIR attacks) with success adjudication via a trusted judge model (e.g., GPT-4o-mini StrongREJECT scoring).
Agentic-level: Transfer of model-level successful attacks into the agentic action trace at each possible injection point (human-message, tool input, inter-agent message), coupled with iterative, context-aware refinement that exploits full agentic state (history, tool, memory).

Comparative analysis of ASR, attack transferability, and context-dependent success across these modalities quantifies the agentic gap and reveals deployment-specific vulnerabilities (Wicaksono et al., 5 Sep 2025, Wicaksono et al., 21 Sep 2025).

4. Empirical Findings and Quantitative Gaps

Across open and closed models (GPT-OSS-20B, Gemini-2.0-flash), empirical evaluation reveals:

Model	Model-Level ASR	Agentic Tool-Context ASR	Delta (%)
GPT-OSS-20B	39.47%	46% (tool), 37% (non)	+24% (tool uplift)
Gemini-2.0-flash	50.00%	24% (tool), 15% (non)	+60% (tool uplift)

Agent-transfer operations: The highest ASR (GPT: 67%; Gemini: 35%), highlighting lateral movement as the dominant attack surface.
Code execution and memory ops: Substantially elevated risk in agentic context (51% and 30%).
Transferability: Direct model-level attacks degrade when ported to agentic context (GPT: 57% agentic vs. 39.47% model-level; Gemini: 28% agentic vs. 50% model-level).
Context-aware, iterative attacks: Recover and surpass baseline agentic ASR (up to 65% for GPT, 45% for Gemini), breaking objectives that were model-level hard (Wicaksono et al., 5 Sep 2025).

These findings establish that canonical safety evaluations systematically underestimate true deployment risks and that only agentic-level, context-aware probing uncovers the full attack surface.

5. Design and Governance Implications

Agentic gap analysis mandates a paradigm shift in safety engineering, evaluation, and operational governance:

Integrate observability and traceability: Instrument agent executions using action/component graph frameworks, ensuring all agentic operations—including tool calls, memory updates, and inter-agent messages—are captured and auditable.
Contextualize red-teaming: Red-teaming must move beyond static model probes to encompass injected, adaptive attacks at all action points within multi-agent or tool-augmented workflows.
Develop agentic-specific defenses: Runtime guardrails for tool orchestration, memory integrity checks, and agent-transfer filtering must be implemented at the control and orchestration level, not solely via prompt engineering.
Continuous iterative evaluation: Routine deployment of context-aware, iterative attack campaigns is necessary to reflect real-world threat actor sophistication and evolving vulnerability profiles (Wicaksono et al., 5 Sep 2025).

6. Broader Scope: Interpretability, Verification, and Societal Risks

The agentic gap is not confined to isolated safety metrics; it intersects with interpretability, system-level accountability, and risk assessment:

Interpretability and oversight gaps: Causal tracing and system-level explanation frameworks reveal further discrepancies between the true decision pathway and what post hoc explanation tools can reconstruct (the interpretability gap). Only continuous, graph-based causal monitoring can close this deficiency (Zhu et al., 23 Jan 2026).
Governance-to-action closure: Ensuring that governance obligations map directly to orchestrated agent actions—and produce evidence trails for post-hoc attestation—is essential. Otherwise, obligations sit in an unenforceable closure gap (Koch et al., 18 Apr 2026).
Scientific and economic domains: In applied science, the agentic gap equates to the non-coverage of adversarial/falsification experiments—claims are advanced without thorough attempts at refutation, leading to non-robust findings (Fa et al., 23 Apr 2026). In economic, IP, and policy domains, the gap manifests as fractured accountability, "moral crumple zones," regulatory misfit, and misaligned incentives (Mukherjee et al., 1 Feb 2025, Osmond et al., 28 Mar 2026).

7. Future Directions and Recommendations

Comprehensive closure of the agentic gap requires:

Standardized, deployment-aware benchmarks: Task and safety evaluations must be aligned to agentic realities, covering multi-tool, multi-agent, and long-horizon scenarios.
Observable and transparent orchestration: Adoption of traceable control frameworks (e.g., AgentSeer, knowledge graph schemas) as standard design primitives.
Context-aware agentic red-teaming: Automated, adaptive attack campaigns embedded into the CI/CD pipeline, exploiting the full state and tool surface.
Governance protocols mapped to orchestration: Embedding obligations into orchestration rules, with minimum action-evidence bundles ensuring attestation and compliance (Koch et al., 18 Apr 2026).
Cross-disciplinary collaboration: The agentic gap exposes not only technical but regulatory, legal, and organizational deficiencies, necessitating integrated frameworks spanning engineering, compliance, and ethical oversight.

Agentic gap analysis thus serves as a foundational methodology for evaluating, securing, and governing the next generation of LLM-based agents as they move from static model outputs to fully integrated, mission-critical autonomous deployments (Wicaksono et al., 5 Sep 2025, Wicaksono et al., 21 Sep 2025, Fa et al., 23 Apr 2026, Koch et al., 18 Apr 2026, Zhu et al., 23 Jan 2026).