Agentic AI in Banking: A 4-Stage AI Maturity Framework from Automation to Autonomy
- Sujin Joseph
- 3 minutes ago
- 9 min read
How banks can evaluate their AI maturity and chart a practical path from automation to autonomous systems
The last two years have seen generative AI move from the exploratory stages to the core of enterprise transformation. According to KPMG, 42% of organizations have now deployed at least some AI agents, and by Q3 2025, the majority have moved past experimentation with 55% actively piloting agents in production environments.
In banking, interest is concentrating in specific areas:
Back-office operations: Fraud investigation, complaints processing, and credit support
Customer interactions: Conversational AI for service delivery
Frontline enablement: Real-time insights and automated workflows for colleagues and front-line staff
Agentic AI systems operate through autonomous agents that can take independent action.
Unlike traditional automation that follows predetermined scripts, these agents possess genuine agency i.e. the capacity to independently initiate workflows, develop execution plans and carry out actions aligned with defined objectives.
They are typically powered by large language models and enhanced with capabilities including retrieval-augmented generation, integration with external tools and APIs, reasoning frameworks, and memory systems for maintaining context - designed to work proactively with minimal human direction.
However, real-world agentic AI applications in banking remain uncommon—or more accurately, cautiously emerging. The familiar challenges include:
Evolving regulatory frameworks for AI oversight
Model-related risks from misspecification and deceptive alignment
Privacy and data protection complexities
Systemic bias concerns
However, there's another reality with equal weight: banks aren't starting from the same place on the AI maturity spectrum.
Some have deployed sophisticated LLM assistants and copilots. Others are still implementing basic RPA. This heterogeneity fundamentally shapes the degree to which institutions must overhaul legacy systems and data integration protocols to embed agentic AI in core processes.
This progression can be best understood through the “AI Autonomy Ladder” framework.
The AI Autonomy Ladder: Four Levels from Automation to Agentic Autonomy
The journey from basic automation to fully agentic AI unfolds across four distinct levels, each representing a meaningful step up in reasoning capability and autonomy - along with corresponding increases in AI risks and governance complexity.
This progression matters in banking, where 80–90% of data sits in unstructured formats that resist conventional automation.
Understanding these levels helps leaders assess not just where they are, but what capabilities and controls they need to advance.
The four levels are:
Level 1: Scripted Automation (rule-based, no learning)
Level 2: Cognitive Assistance (pattern recognition, learns via retraining)
Level 3: Contextual Reasoning (LLM copilots, in-context learning)
Level 4: Agentic Orchestration (multi-agent systems, continuous adaptation)
To understand what these levels mean in practice, consider how they transform a common banking workflow like customer KYC or credit underwriting.
Level 1: Scripted Automation - Rule based Automation Without Context Awareness
The first stage is defined by rule-based automation without context awareness. At this level, logic is entirely hand-coded. RPA bots and template-based OCR tools execute repetitive, predictable tasks such as document handling, data entry, and field validation.
The defining characteristic at this stage: these automation systems don't learn.
Instead they execute fixed steps in a predetermined sequence, limited by pre-configured logic paths and templates.
Whenever document formats change or edge cases emerge, manual intervention or validation is required to handle exceptions.
Key Characteristics:
Dimension | Level 1 Capability |
Autonomy level | None (executes fixed steps) |
Learning/adaptation | Static |
Scope of use | Narrow, repetitive, rules-based |
Decision-making | None |
Memory across sessions | None |
Explainability | High (list of steps) |
Governance need | Basic access control and compliance |
How Scripted Automation Works in Customer KYC Processes
Consider a standard KYC workflow: A customer submits identity documents. RPA combined with OCR validates predefined fields against expected format templates.
The system can confirm that a name field contains text and a date field contains a valid date. But it cannot interpret whether information across documents is consistent or whether signatures look authentic or whether supporting documentation is sufficient.
Challenges:
Analysts must resolve all exceptions manually.
Cleaned data pushes to downstream workflows, but the operational knowledge from exception handling - the patterns analysts recognize, the judgment calls they make, never feeds back into the system.
Governance and Explainability at Level 1
This is the simplest level to manage with high explainability: every action traces to a specific rule. Controls focus on basic access management and compliance verification.
However this simplicity has a big trade-off: system delivers faster throughput on predictable, high-volume tasks, but fails wherever cases deviate from templates.
Level 2: AI-Assisted Automation with Pattern Recognition
The second stage reflect the first true shift: automation begins to interpret information rather than simply process it.
Machine learning and natural language processing models identify patterns from unstructured data and surface anomalies based on probability rather than rigid rules.
At this level, AI systems move from fixed rules to probability-based confidence scores. Instead of binary pass/fail outcomes, the system produces assessments of likelihood.
This enables analysts to focus only on cases where the model has low confidence, rather than reviewing every transaction.
Key Characteristics:
Dimension | Level 2 Capability |
Autonomy level | Low (produces scores/text, human embeds action) |
Learning/adaptation | Retrained offline |
Scope of use/ Level of generalization | Single domain (e.g., credit score evaluation) |
Decision-making | Outputs probability |
Memory across sessions | Model weights only |
Explainability | Medium (dependent on input features) |
Governance need | Controls for ethical use, fairness, transparency, bias management |
How AI/ML assisted Automation Works in KYC Processes
In the same customer KYC workflow, when a customer submits identity documents, an ML model extracts fields and assesses its confidence in each extraction.
Documents with high-confidence extractions proceed automatically; only those flagged as low-confidence route to an analyst for review.
The system now learns from human decisions. When an analyst approves a correction, that feedback updates the model's understanding. Over time, the system becomes more accurate on the specific document types and edge cases that the institution encounters most frequently.
Challenges:
Low levels of autonomy: system might flag a document as potentially fraudulent, but it cannot decide what to do about that assessment.
Learning at this level occurs through periodic offline retraining: The model improves, but not in real time. Updates happen on a scheduled basis, meaning the system cannot adapt to new patterns or emerging risks
Narrow generalization: Each model is trained for a specific task, such as evaluating credit scores or detecting particular fraud patterns, and cannot generalize beyond its training domain.
Governance and Explainability at Level 2
Governance requirements expand at this level:
Beyond basic compliance, institutions must implement controls for fairness, transparency, and bias management. As model's reasoning depends on features and weights rather than explicit logic, explainability becomes more complex.
For banking leaders, Level 2 represents a genuine lift in operational efficiency - but it also introduces new risks that require regulatory compliant oversight.
Level 3: LLM Copilots and Contextual Reasoning
This corresponds to embedding large language models into banking workflows. These LLM-based systems can plan, retrieve information, and execute tasks through APIs. They are capable of reasoning across multiple steps using in-context learning and retrieval-augmented memory.
This is a step-up from pattern recognition to reasoning with context.
Rather than simply identifying that a data point matches a trained pattern, the system can understand relationships between documents, reconcile conflicting information, and generate explanations for its assessments.
It handles multidomain conversations and can draw on broad knowledge bases to contextualize specific tasks.
Key Characteristics:
Dimension | Level 3 Capability |
Autonomy level | Medium (can draft and call APIs, expects user prompt) |
Learning/adaptation | In-context learning / RAG memory |
Scope of use/ Level of generalization | Broad knowledge, multidomain conversation |
Decision-making | Suggests actions |
Memory across sessions | Short-term conversation history |
Explainability | Medium to low (sensitive to prompt construction) |
Governance need | Model risk controls for accuracy and misinformation |
LLM Automation & Contextual Reasoning in Loan Underwriting
When a customer submits a loan application and the loan officer initiates onboarding - RAG-powered copilots take over substantial portions of the analytical work.
These copilots extract and validate data across multiple documents such as - tax returns, bank statements, employment verification, salary slips/ paystubs - identifying inconsistencies and gaps that would require significant analyst time to surface manually.
They fetch financial history from external sources through tax APIs and credit bureau APIs, assembling a comprehensive picture of the applicant's financial position.
They are able to validate this information against the institution's loan sanction guidelines and generate explanations for their assessments.
Finally, the underwriter receives a prepared package: not just extracted data, but a reasoned analysis with documented logic. The human reviewer can focus on judgment calls and policy decisions rather than data assembly and basic validation.
Challenges:
Medium autonomy: LLM copilots can draft documents, call APIs, and synthesize information across sources - but they expect prompts to initiate actions.
Memory is limited to short-term conversation history: The system remembers context within a session but does not build persistent knowledge about specific borrowers, relationship managers, or institutional patterns over time.
Governance and Explainability at Level 3
Governance at this level requires formal AI oversight as part of risk management framework. Explainability decreases as outputs depend on model weights and inner representation that are opaque to organizational users.
In addition to this, the output is highly sensitive to prompt construction and the placement of words in the input. Two slightly different phrasings of the same question can produce substantively different responses, making it difficult to ensure consistent application of policy.
The risk of misinformation increases - LLMs can generate convincing explanations for incorrect conclusions, and without robust validation frameworks.
Moreover, these errors may not be caught before they influence decisions. For institutions operating at this level, investment in accuracy monitoring and human review protocols is essential, not optional.
Level 4: Multi-Agent AI Systems and Autonomous Orchestration in Banking
This stage represents the full realization of agentic AI. At this level, interconnected AI agents can plan, act, and adapt without human prompting.
Each agent can decompose goals into subtasks, choose appropriate tools or APIs, and coordinate outcomes with other agents.
The defining characteristic of this level is that learning shifts from periodic retraining to real-time adaptation.
The system improves continuously based on outcomes, updating its heuristics and decision patterns as it processes new information. Human oversight does not disappear, but it migrates from directing individual tasks to monitoring system behavior through governance dashboards.
Key Characteristics:
Dimension | Level 4 Capability |
Autonomy level | High |
Learning/adaptation | Continuous feedback and real-time adaptation |
Scope of use/ Level of generalization | Cross-functional, multi-system orchestration |
Decision-making | Autonomous within guardrails |
Memory across sessions | Persistent, shared across agents |
Explainability | Low (complex agent interactions) |
Governance need | Real-time monitoring dashboards, intervention protocols |
Multi-Agent Orchestration in Loan Underwriting
In a loan underwriting workflow, the customer's loan application triggers a coordinated response from multiple specialized agents, each with distinct responsibilities:
Agent A focuses on Document Intelligence: It classifies income documents, extracts relevant information, and critically - shares errors back to the system for retraining. When it encounters a document format it handles poorly, that experience improves future performance.
Agent B handles Risk and Credit Evaluation: It fetches data from credit APIs, scores risk profiles based on comprehensive financial analysis, and recommends loan terms. Unlike a LLM copilots, it does not wait for a human to request this analysis - it initiates the work based on the application trigger.
Agent C serves as the Decision Coordinator: It aggregates outputs from the other agents, simulates loan outcomes under different scenarios, and updates shared heuristics based on results. When a loan performs differently than predicted, that feedback refines the models that all agents use.
All outputs flow to an oversight dashboard where human reviewers monitor patterns, intervene in flagged cases, and adjust system parameters.
Governance and Explainability
Governance at Level 4 is fundamentally different from earlier stages. Explainability is low - the interactions between agents, the continuous updating of heuristics, and the complexity of multi-step reasoning make it difficult to trace exactly why a specific outcome occurred.
Institutions operating at this level require real-time monitoring dashboards that track system behavior across multiple dimensions: accuracy, consistency, fairness, and alignment with policy.
They need clear accountability and AI risk management frameworks that define who is responsible when an autonomous system makes a consequential error. And they need intervention mechanisms that allow humans to override, pause, or retrain agents when behavior drifts outside acceptable bounds.
For most banks, Level 4 represents an aspiration rather than current reality. The technical capabilities exist, but the governance infrastructure to safely operationalize autonomous systems in regulated environments is still being developed.
Conclusion: Assessing Readiness Before Advancing
The question for banking leaders isn't simply "how do we implement agentic AI?" but rather "what is our organization ready to govern?"
Understanding your position on the AI autonomy ladder enables clear-eyed assessment of:
Where you can create value with appropriate controls in place
Where gaps between capability and governance would introduce unacceptable risk
What sequence of investments—technical, operational, and institutional—makes sense
Banks that succeed with agentic AI will be those that recognize this as a progression requiring investment at each stage: not only in technology, but in the frameworks for trust, oversight, and accountability that make autonomous systems viable in regulated environments.
The autonomy ladder provides a practical framework for that assessment. The first step is determining where you stand today.
Ready to assess where your organization sits on the AI autonomy ladder?
Sentient Concepts partners with banks across APAC and the United Kingdom to design and implement AI solutions tailored to each stage of maturity.
From intelligent document processing and advanced analytics to agentic orchestration and hyper-automation, we deliver end-to-end solutions—from strategy through production—that drive measurable outcomes while meeting governance requirements at every level.
Learn how we can help you assess your current position on the autonomy ladder, identify high-impact use cases, and build a practical roadmap forward.