Agentic AI in financial services: What you need to know before building

Many financial institutions use AI somewhere: fraud detection, chatbots, document processing. The list goes on, and the investments are growing.

But fewer institutions capture real value from these investments. Many generative AI projects don't make it past proof-of-concept into production outcomes, and implementation delays are common, often driven by legacy integration and governance challenges.

The difference often comes down to infrastructure and scoping, not the AI model itself. The institutions getting results started with focused use cases, clear metrics, and proper infrastructure rather than enterprise-wide deployment. They treated agentic AI as fundamentally different architecture requiring corresponding infrastructure changes.

This article covers what agentic AI actually is, the infrastructure requirements for production deployments, and how to get started without stalling.

What is agentic AI & how is it different?

Traditional automation follows predefined workflows. GenAI creates content based on prompts. Agentic AI does both—and goes further: it reasons through problems, takes actions, observes what happens, and adjusts its approach based on results. Instead of waiting for instructions at each step, agentic systems pursue goals autonomously.

The foundation is ReAct (Reasoning and Acting), a continuous loop where agents think through a problem, execute operations like API calls or database queries, observe results, then decide what to do next. Agentic systems can log actions, tool calls, retrieved context, and approvals, which is useful for audit trails.

Here's what separates agentic AI from what you're probably already using:

• Traditional automation executes static rule-based decision trees. Your payment processing system follows predefined rules: if transaction amount exceeds $10,000, flag for review. These rules typically don't adapt based on context without manual updates.
• GenAI creates content. Your customer service chatbot generates responses based on prompts. It answers questions, but on its own it doesn't pursue persistent goals or coordinate actions across systems.

Agentic AI orchestrates workflows autonomously. The LLM coordinates tool usage and workflow execution across systems, querying databases, cross-referencing models, checking regulatory requirements, then recommending actions or escalating to human review based on risk profiles.

Financial institutions can implement these patterns with different levels of human oversight: human-in-the-loop review for high-risk transactions, autonomous execution for low-risk determinations. The key is maintaining compliance while supporting real-time response.

Rule-based systems still work well for deterministic, well-defined workflows. But agentic AI is often better suited for workflows that demand cross-system reasoning, richer context awareness, and dynamic adaptation within complex workflows.

High-impact use cases

Not every workflow benefits from agentic AI. These four use cases tend to deliver measurable ROI because they combine high transaction volumes, strict latency requirements, and regulatory complexity, though results vary by implementation.

• Fraud detection & security systems need to make decisions fast. High-performance payment flows often budget 10–50ms for fraud scoring decisions. Agents codify regulatory logic, check transactions in real time against anti-money laundering (AML) and know your customer (KYC) requirements, and generate complete audit trails.
• Customer support automation often delivers clear cost benefits. Institutions report meaningful workload reduction when agents handle routine requests and escalate edge cases, with customers in some deployments preferentially engaging with the AI agent over traditional channels. A common pattern is two-phase deployment: pilot validation followed by scaled production.
• Compliance & regulatory automation addresses a persistent pain point. AI agents can support real-time transaction checking against AML and KYC requirements with codified regulatory logic, integrated with existing platforms. The value extends beyond cost reduction to risk mitigation through consistent, auditable execution.
• Customer onboarding & KYC acceleration is a major focus area. Customer onboarding and KYC automation rank among the top use cases for agentic AI in banking because they can accelerate time-sensitive customer acquisition while maintaining regulatory compliance.

These use cases work in part because institutions built proper infrastructure first. It's often difficult to retrofit low-latency fraud detection onto a disk-based database architecture. Scaling conversational AI on basic, request-level caching alone becomes increasingly challenging.

What it takes to make agentic AI work

Infrastructure requirements vary dramatically by use case, and getting them wrong is often what stalls projects.

Latency requirements by workload

Latency targets vary by use case. In high-scale payment environments, fraud scoring budgets are often 10–50ms. For customer service AI, response times under ~100ms feel instantaneous, while responses under ~1 second maintain conversational flow. Exact thresholds depend on business and regulatory requirements.

Where Redis fits

Redis delivers sub-millisecond latency for vector search operations in many workloads through in-memory storage. The Redis Agent Memory Server—an open-source dual-tier memory system—manages both short-term conversational context and long-term episodic memory for user preferences across conversation threads. For financial agents, this means maintaining compliance audit trails while adapting to customer patterns without sacrificing speed.

Vector search & RAG configuration

RAG (retrieval-augmented generation) improves LLM responses by retrieving relevant context from your data before generating an answer. RAG implementations vary widely, but common starting points include top K around 10–20, chunk sizes in the low-thousands of tokens, and modest chunk overlap. The optimal values depend heavily on your data, model, and retrieval goals, so you'll usually need to experiment.

In Redis, vector indexes use specialized data structures like Hierarchical Navigable Small World (HNSW) for approximate nearest-neighbor search and FLAT for exact matching, both designed for similarity search in high-dimensional vector spaces. Redis benchmarks show sub-millisecond vector search latency for many workloads, with sub-100ms latency at scale when searching millions of vectors on Redis Cloud.

Real-time data pipelines

Agentic systems need continuous data flow—market feeds, transaction streams, customer events. Redis Streams provides an append-only log with consumer group semantics, so multiple agents can process the same stream independently while Redis tracks state. When an agent fails mid-task, pending messages can be reassigned to another consumer using Redis Streams consumer groups (e.g., via XAUTOCLAIM/XCLAIM), so processing can resume without losing work. For financial workflows requiring stream processing and fast state lookups, Redis handles both in one platform.

Keeping agentic AI safe & compliant

Financial services AI operates under multiple regulatory frameworks. Understanding which rules apply to your use case is the first step toward compliant deployment.

US regulatory requirements

The Federal Reserve's SR 11-7 requires model risk management for "quantitative methods used for decision-making," which most institutions interpret as covering modern AI systems. This means model validation with independent review and board-level governance. The guidance notes that "weak governance undermines the effectiveness of model risk management, even if development, implementation, use, and validation are satisfactory."

For trading applications, FINRA requires firms to supervise AI algorithms under rules 5210, 6140, 2010, and SEC Market Access Rule. SEC Rules 17a-3 and 17a-4 require complete records of specified business communications and records, creating recordkeeping obligations that extend to AI-generated outputs in many broker-dealer contexts.

International & state frameworks

The European Union Artificial Intelligence Act (EU AI Act) classifies AI systems used for creditworthiness assessment and credit scoring as high-risk, which directly affects many financial services use cases. Banks must maintain up-to-date AI system inventories with risk classification, ongoing performance monitoring, and complete risk management policies. In the US, Colorado's AI Act (SB24-205) is scheduled to take effect June 30, 2026.

Building for explainability

These frameworks share a common requirement: AI-driven decisions must be explainable and auditable. Agentic architectures support this through logged tool calls, retrieved context, and approval workflows rather than raw model reasoning. Redis Agent Memory Server provides a dual-tier memory layer (working + long-term) so you can persist conversation/task state and retrieve history quickly; pair it with application logging of tool calls, retrievals, and approvals to meet audit requirements.

Human oversight patterns

Systems can function with varying human involvement: human-in-the-loop for high-risk decisions, autonomous execution for routine operations. The key is building these controls into system architecture from day one. Retrofitting compliance afterward tends to be harder and more expensive.

Getting started without stalling

Agentic AI adoption is expected to accelerate over the next two years. The good news: you don't need a multi-year roadmap to see results. Allianz's claims processing improvements came from a focused 100-day pilot, not an enterprise transformation.

Start with infrastructure & governance

Begin with platform-aware infrastructure that supports AI at scale and a central governance model for AI agent operations. Map your use cases to identify high-value, low-complexity pilots you can launch quickly.

Infrastructure readiness typically includes real-time monitoring for agent activity, cloud platform integration, data governance frameworks, and security controls. CIOs expect AI agents will operate under a central governance model. Accenture recommends safeguards such as multi-agent validation for sensitive tasks and compliance integration.

Pilot & measure outcomes

Focus on business outcomes from day one: safer payments, faster credit decisions, reduced fraud, improved customer relations. AI agents are already improving performance in software engineering, risk management, and customer service. Pick one area, measure results, iterate.

What successful implementations share

Clear strategic vision, strong infrastructure, centralized governance, human-centered change management, and continuous monitoring. Institutions that report quarterly outcomes help teams see purpose rather than just tools.

Building AI in financial services on the right foundation

Agentic AI in financial services represents a fundamental shift from automation to autonomous reasoning. Institutions with the right infrastructure are already seeing results across customer service operations, claims processing, and fraud detection at scale.

The infrastructure challenge is real. Redis provides a unified real-time data platform that addresses agentic AI requirements through a single layer. Vector search delivers sub-millisecond latency for semantic memory in typical workloads. Redis LangCache can deliver up to 15x faster responses per cache hit and cut LLM API costs by up to 73%. The Redis Agent Memory Server manages both short-term conversational context and long-term episodic memory with the audit trails financial services demands.

Financial institutions already use Redis for the real-time infrastructure agentic AI builds on. In adjacent high-volume customer support environments, Asurion improved response times by more than 50% using Redis-powered semantic routing to direct customer queries to the right tools and agents. In published case studies, Axis Bank reports 76% faster app performance and TransNexus reports 95% reduction in fraud detection time—the foundational speed that agentic workflows depend on.

Try Redis free to test vector search, semantic caching, and agent memory patterns with your workload, or talk to our team about building agentic AI infrastructure that delivers results.