Agentic Reasoning (System 2) in Autonomous AI

• Agentic Reasoning (System 2) is a deliberative, multi-agent cognitive process that integrates various AI models to produce consensus-driven, auditable decisions.
• It employs explicit uncertainty quantification and policy-driven governance to flag ambiguous reasoning and enforce safety and compliance.
• Architectural designs leverage parallel model engagement and structured audit trails, enhancing robustness and transparency in autonomous AI deployments.

Agentic reasoning, referred to as “System 2” in dual-process cognitive theory, denotes deliberative, serial, and reflective reasoning in autonomous AI systems. Distinguished from rapid, heuristic “System 1” responses, agentic reasoning orchestrates multi-step planning, critical evaluation, explicit uncertainty quantification, and end-to-end governance—often through the parallel engagement of LLMs, vision-LLMs (VLMs), domain-specific tools, and meta-reasoning agents. Recent architectures operationalize these principles to produce AI agents that are auditable, robust, and aligned with production-grade safety and explainability requirements (Bandara et al., 25 Dec 2025).

1. Formalization and Theoretical Foundations

Agentic (System 2) reasoning is best understood as a multi-component cognitive process with the following formal attributes (Bandara et al., 25 Dec 2025, Alenezi, 11 Feb 2026, Lowe, 2024):

• Deliberative, Multi-Agentic Structure: A set of $N$ heterogeneous reasoning agents (LLMs, VLMs) $A_i$ take a shared context $(P, C)$ as input and each produce candidate outputs $o_i$ with internal confidence scores $c_i$. Requisite isolation enforces independence, avoiding premature convergence.
• Consensus-Driven Decision Rule: A governance agent computes a weighted consensus score

$$F(o) = \sum_{i=1}^N w_i\,\mathbb{I}[o_i=o], \quad w_i = \frac{c_i}{\sum_j c_j}$$

for each distinct $o$ and selects $o^* = \arg\max_{o} F(o)$, subject to policy constraints $P(o)=\mathrm{true}$. Alternative strategies include weighted majority and agreement-matrix clustering.

• Uncertainty Quantification: Inter-model disagreement is measured via the entropy of normalized support,

$$H = -\sum_{o} p(o) \log p(o), \quad p(o) = \frac{\sum_{i:o_i=o} w_i}{\sum_j w_j}$$

with high $H$ indicating ambiguous or contentious reasoning steps, flagging these for review or escalation.

• Governance and Auditing: A dedicated reasoning agent enforces a policy predicate $P(o)$ representing safety, explainability, and compliance constraints. All intermediate artifacts, including outputs, confidences, and policy flags, are recorded for full auditability (Bandara et al., 25 Dec 2025).

2. System Architectures, Modules, and Computational Recipes

The architectural pattern underpinning agentic reasoning features the following subsystems (Bandara et al., 25 Dec 2025, Alenezi, 11 Feb 2026, Dao et al., 27 Jan 2026):

Component	Role	Example Implementations
LLM/VLM Agents	Generate independent candidate outputs	GPT-family, Llama, Qwen2-VL
Reasoning/Governance Agent	Consolidate, validate, and select among outputs (meta-reasoning)	GPT-oss, bespoke governance LLM
Tool & Service APIs	Interface for retrieval, calculation, external environment interaction	Calculators, knowledge bases
Orchestration Layer	Broadcasts prompts, collects outputs, enforces workflow sequencing	Custom orchestration software

The process unfolds as: (1) context broadcast to $N$ agents; (2) candidate output collection; (3) weighted policy-constrained consensus selection; (4) audit logging and explainable report generation.

Algorithmic pseudocode (Bandara et al., 25 Dec 2025):

Input: Candidate outputs {(o_i, c_i)}_{i=1}^N, policy P Compute normalized weights w_i ← c_i / Σ_j c_j Identify distinct outputs O = unique({o_i}) For each o in O: score[o] ← Σ_{i: o_i = o} w_i valid[o] ← P(o) Select o* ← argmax_{o in O and valid[o]=true} score[o] If no valid o exists: raise Alert("No policy-compliant consensus") Return o*

Complexity is $O(NM)$, where $M$ is the average output length per agent.

3. System 2 Properties: Deliberation, Uncertainty, and Governance

System 2 agentic reasoning incorporates several key attributes (Bandara et al., 25 Dec 2025, Lowe, 2024, Shang et al., 28 Aug 2025):

• Deliberation: Multiple independent models propose alternative solutions, explicitly exposing model diversity and enabling rejection of spurious reasoning paths.
• Explicit Uncertainty Handling: Quantitatively surfaces disagreement (entropy), suspends critical decisions in high-uncertainty regions, or prompts human intervention.
• Governance: Centralized meta-reasoning enforces explicit policy and safety constraints, mitigates hallucination via cross-agent fact-checking, and produces structured, explainable outputs.
• Auditability: Intermediate reasoning steps, agent-level confidences, source citations, and all policy evaluations are fully logged, supporting rigorous traceability.

These properties are crucial for applications where downstream actions or decisions demand high assurance, transparency, and regulatory compliance.

4. Empirical Evaluation and Performance Benefits

Empirical studies across diverse agentic workflows—ranging from news podcast generation to medical vision analysis—demonstrate strong benefits (Bandara et al., 25 Dec 2025):

Metric	Consensus-Driven (System 2)	Single Model Baseline
Hallucination Rate	35–50% reduction	Baseline
Auditability	100% intermediate outputs auditable	Limited
End-User Trust Score	4.5/5	3.2/5
Transparency	Entropy logs surfaced ambiguity in 20% cases	Largely unreported

These results substantiate that consensus-driven agentic reasoning provides tangible robustness, explainability, and operational trust—crucial for production-grade deployment.

5. System-Theoretic and Control Perspectives

Agentic reasoning is closely connected to control-theoretic and BDI (Belief-Desire-Intention) frameworks (Alenezi, 11 Feb 2026, Dao et al., 27 Jan 2026):

• Control Loop Formalism: At each timestep $t$:

$$\Delta B_t = f_1(E_t, B_{t-1}); \quad B_t = B_{t-1} \oplus \Delta B_t; \quad P_t = \pi(B_t, D); \quad I_t = \text{commit}(I_{t-1}, P_t); \quad A_t = \alpha(I_t)$$

Belief, desire, and intention states are updated via environmental feedback, and plans/actions are generated accordingly.

• Typed Tool Contracts and Policy Gates: Every tool integration is governed by JSON-Schema/OAS contracts, with preconditions/postconditions enforced at runtime.
• Multi-Agent Topologies: Architectures include orchestrator–worker, router–solver, hierarchies, and market-like swarms, each with specific mitigation strategies for their failure modes.
• Systems-Theoretic Patterns: Core agentic capacities—deliberative planning, dynamic adaptation, inter-agent communication—are decomposed into reusable patterns (e.g., Integrator, Recorder, Planner), each responsible for preventing distinct classes of System 2 failures such as hallucination, context drift, or planning staleness (Dao et al., 27 Jan 2026).

6. Limitations, Challenges, and Future Directions

Despite empirical gains, important challenges persist (Bandara et al., 25 Dec 2025, Alenezi, 11 Feb 2026):

• Verifiability and Formal Guarantees: There is an ongoing need for formal proof-carrying actions, regression test benches, and conformance suites for evolving tool graphs.
• Interoperability Standards: Establishing minimal safe agent–agent and agent–tool protocols is crucial for scalable and composable autonomy.
• Safe Autonomy and Budgeted Reasoning: Enforcing strict quotas on compute, tokens, and cost, together with human-in-the-loop and simulated (“sandbox-first”) execution, remains an open area.
• Auditability and Governance: Automation of policy audit trails and lineage, end-to-end tracing, and regulatory reporting mechanisms requires further systematization.
• Bias and Hallucination Mitigation: While consensus and cross-validation reduce certain failure modes, open-domain and adversarial settings continue to challenge robust performance.

7. Synthesis: System 2 as Production-Grade Consensus Reasoning

Consensus-driven, agentic reasoning concretely instantiates “System 2” principles by tightly coupling multi-model deliberation, formal consensus aggregation, explicit uncertainty quantification, governance-layer policy enforcement, and comprehensive auditability. By structuring reasoning as an orchestrated, modular workflow—rather than as a sequence of isolated black-box decisions—these systems align AI decisions with the high standards required for autonomy, explainability, and operational integrity in real-world applications (Bandara et al., 25 Dec 2025, Alenezi, 11 Feb 2026).

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References:

• (Bandara et al., 25 Dec 2025)
• (Alenezi, 11 Feb 2026)
• (Lowe, 2024)
• (Shang et al., 28 Aug 2025)
• (Dao et al., 27 Jan 2026)