30 articles · Filter by topic, level, or domain
Production Agentic AI systems fail in ways that unit tests and demo environments cannot catch. A comprehensive framework for evaluation, tracing, and drift detection that keeps autonomous agents reliable at scale.
Traditional MRM frameworks were designed for static, batch models — not autonomous agents that evolve, chain actions, and interact with production systems. A framework for extending three-lines-of-defence to Agentic AI.
When AI agents transact with each other autonomously, payments infrastructure needs to evolve. A look at the emerging A2A commerce stack, identity requirements, and what this means for financial services architecture.
Prompt injection and memory poisoning are not theoretical threats in production multi-agent banking systems — they're live attack vectors. A technical defence architecture with a banking case study.
At enterprise scale, token costs compound fast. A technical deep dive into caching strategies, model routing, context compression, and the architecture decisions that can cut your GenAI infrastructure bill by 60–80%.
Most Agentic AI pilots in banking never reach production — not because the technology fails, but because the transformation programme lacks the right structure. A detailed 18–36 month roadmap for banks that are serious about scaling.
Every agent in a production multi-agent system needs a cryptographically-verifiable identity, scoped permissions, and a full audit trail. A technical blueprint for zero-trust agent identity in financial services.
A rigorous, criteria-driven framework evaluation for practitioners building Agentic AI in regulated environments — covering state management, auditability, security controls, and vendor lock-in risk.
The EU AI Act places Agentic AI systems used for credit and insurance decisioning in the highest-risk category. A complete compliance architecture — covering conformity assessment, data governance, audit trails, and human oversight.
A practitioner's reference architecture for using Agentic AI to progressively modernize core banking — without the risk of a big-bang core replacement. Covers the integration layer, domain agent design, and the failure patterns that derail these programs.
Before any autonomous agent executes a high-stakes banking action, a guardian agent should verify intent, check policy, and require human approval. A pattern that makes Agentic AI safe enough for production in financial services.
Agentic AI can dramatically accelerate credit underwriting — but in a regulated environment, human oversight is non-negotiable. A reference architecture for a compliant, agent-augmented lending workflow.
Frontier LLMs get the press, but SLMs — fine-tuned, fast, and deployable on private infrastructure — are often the better choice for enterprise use cases. When and how to choose them.
A deep dive into designing fraud detection pipelines that combine real-time ML inference with agentic reasoning — covering explainability requirements, false positive management, and regulatory defensibility.
Model Context Protocol and Agent-to-Agent communication are becoming the connective tissue of enterprise Agentic AI. A practical guide to what they are, how they work together, and what to watch out for in production.
The next generation of wealth management doesn't use a robo-advisor — it uses a team of always-on agents that proactively monitors, advises, and executes. A reference architecture for building one.
A structured, opinionated comparison of three leading Agentic AI platforms through the lens of a regulated financial services environment — evaluated on governance, auditability, integration, and total cost of ownership.
A practitioner's blueprint for building a KYC/AML agent that meets regulatory requirements while dramatically cutting onboarding time — covering data flows, model selection, explainability, and audit trail design.
The three fundamental orchestration architectures for multi-agent systems — when to use each, how to implement them, and the failure modes that practitioners consistently underestimate.
Moving beyond 'retrieve and generate' to a production-grade, governed knowledge fabric — chunking strategies, hybrid retrieval, re-ranking, access control, and the architecture decisions that separate demos from deployments.
Insurance claims are slow, paper-heavy, and opaque by tradition. AI co-pilots are changing that — automating extraction, routing, and decision-support to deliver faster settlements and fewer errors.
What if every knowledge worker in your bank had a team of AI co-pilots handling research, drafting, and analysis? The '10x banker' idea is moving from thought experiment to practical reality — here's what it looks like.
The market is full of 'AI platforms' but they're not all the same thing. A clear distinction between language models, orchestration frameworks, and full Agentic AI platforms — and why the difference matters for enterprise buyers.
Conversational AI in banking has moved well beyond scripted chatbots. Today's agents handle complex queries, initiate transactions, and escalate intelligently — a look at what the modern experience actually delivers.
Retrieval-Augmented Generation is one of the most important ideas in practical AI — it's how enterprise AI systems get grounded in real, current, trusted information instead of hallucinating. Here's what it actually is.
Modern fraud prevention isn't a rules engine anymore — it's a team of AI agents watching every transaction in real time. A beginner's guide to how they work and what makes them so effective.
Opening a bank account used to take days of paperwork and manual review. AI agents are compressing that to minutes — here's how they work and what the experience change means for customers and banks alike.
Behind the familiar banking apps and call centres, AI agents are already handling millions of decisions every day. A plain-English tour of what they're doing and why it matters.
A clear-headed breakdown of the three most-confused categories in enterprise automation — what each technology actually does, where it works, and why the distinctions matter for your AI strategy.
A jargon-free mental model of what generative AI actually is, how it works under the hood, and why it behaves the way it does — the foundation for everything else on this site.
