66 posts tagged with "Architecture"

Strategy and discovery told MonGo Bank what opportunity to pursue: a trusted hybrid AI service, not a generic cost-cutting chatbot. The next question is harder:

Is the bank actually ready to build, deploy and operate this solution—and which use cases should proceed, pilot, wait or die?

This article is Part II of the Banking Customer-Service AI playbook: readiness, maturity and prioritisation. It continues from Part I: Strategy and Discovery.

Before production, MonGo must show the hybrid customer-service AI is lawful, fair, secure, accountable, controllable and fit for purpose. The goal is not zero risk—it is proportionate controls, measured residual risk and evidence for every decision to continue, restrict or stop.

This article is Part VI of the Banking Customer-Service AI playbook. It follows Part I through Part V.

“Build a generative AI chatbot that reduces customer-service costs” is a common executive request. It is not yet a strategy, a problem statement or an investable use case. This article walks through Part I of the End-to-End AI Solution Engineering Playbook—strategy and discovery—using a realistic retail-banking customer-service scenario.

The worked example is MonGo Bank: a hypothetical retail and small-business bank with millions of customers, a large contact centre, mixed cloud and legacy platforms, and strict regulatory obligations. The goal is not to pick a model. It is to decide where AI should play, how the bank wins, what must be true and what evidence is required before further investment.

Artificial intelligence architecture is moving beyond simple chatbot development. Modern enterprise AI systems must connect to business applications, retrieve organisational knowledge, call external tools, follow security policies, manage long-running tasks and produce outputs that other software systems can reliably process.

The Claude Certified Architect – Foundations, commonly known as CCA-F, focuses on the architectural knowledge needed to design these systems using Anthropic’s Claude platform.

Updated: July 27, 2026

The LangChain Certified Agent Engineer certification is designed to assess whether an engineer can manage the complete lifecycle of a production AI agent—not merely write a basic tool-calling loop.

LangChain describes the certification as covering the entire Agent Development Lifecycle, or ADLC. The exam gives equal weight to four domains:

1. Building agents
2. Testing agents
3. Deploying agents
4. Monitoring agents

Most AI engagements fail not because the organisation lacks frameworks, but because it has too many of the wrong kind. Teams accumulate strategy canvases, maturity models, scoring formulas and governance checklists until the methodology itself becomes the delivery risk. The practical response is not a larger catalogue. It is a core set: enough structure to run end-to-end AI solution engineering, and little enough that practitioners can actually use it.

A software licence is a legal agreement that gives a person or organisation permission to use software in ways that would otherwise be controlled by copyright law. The copyright owner is the licensor, while the person or organisation receiving the permissions is the licensee.

This playbook turns strategy, consulting, architecture, governance, security, delivery, commercial and change frameworks into one practical sequence for taking an AI opportunity from an ambiguous business problem to a scaled, continuously governed production capability.

It is written for AI Solution Engineers, AI Architects, AI Product Managers, enterprise consultants, engineering managers, data and AI leaders, security teams, risk teams and transformation leaders.

The central principle is simple:

Do not begin with a model. Begin with a business outcome, understand the operating context, select the smallest safe intervention that can create measurable value, and build the organisational capability required to sustain it.

The AWS roadmap in the classic one-page diagram provides a strong high-level sequence: begin with cloud fundamentals, IAM, VPC and EC2; continue into S3, SES, Route 53, CloudWatch and CloudFront; add databases and containers; and finish with serverless computing. The diagram also recommends learning by deploying a simple application rather than studying every AWS service independently.

This expanded roadmap turns that sequence into a practical learning system. It explains what each service does, why and when you would use it, which concepts matter, which exercises to complete, how services work together, which production topics are often missing from one-page maps, and how to progress from a simple application to an enterprise-ready AWS platform.

The AWS Learning Roadmap follows a sensible progression: learn cloud fundamentals, identity, networking and compute first; then add storage, databases, containers, serverless and operational services. This Azure roadmap follows the same learning philosophy but expands it into an enterprise-grade programme covering cloud architecture, application delivery, AI engineering, cybersecurity, Responsible AI, regulatory compliance and operating-model design.

Terminology note: Microsoft’s current documentation refers to its unified enterprise AI development platform as Microsoft Foundry. You may still encounter the previous “Azure AI Foundry” name in existing projects, articles and interfaces. Microsoft Foundry brings together models, agents, development tooling and production AI operations as an Azure platform service. (Microsoft Learn)