Modern Software Engineering | Development - AI Agents

Compare bank loans and corporate bonds for a $1 million project over five years, highlighting monthly payments, interest totals, and repayment timelines.

Explore the product development lifecycle from idea generation and screening to concept development and testing, wireframes, and focus group feedback, ending with mvp, market testing, and market entry.

Secure the budget and align vision with the sponsor before discovery. Create a project charter or product definition document, assign a product manager, and form a team to begin discovery.

Break down complex use cases into small, implementable stories—such as importing quotes, price checks, policy availability, and reporting—and track them on a discovery board from idea to done.

Explain how AI agents act as orchestrators that coordinate large language models and tools to automate tasks, using prompt chaining, workflow routing, and feedback loops across databases, APIs, and services.

Compare traditional and automated architectures for Gen AI enrichment, evolving stories from version 1 through 5 via prompts and automation, with API calls, cascade prompting, and Jira export.

Explore enriching content with an API client by calling the ChatGPT API through a post request, tracking tokens, and using tooling to automate prompting and reduce noise.

Explore a well architected product built with microservices and micro-frontends, leverage serverless functions, caching, and containers, and design for stateless, scalable cloud infrastructure with load balancing.

Explore user experience and interface design through user research, journey mapping, wireframes, and prototypes for mobile, PC, and other devices, guiding development with feedback, usability testing, and a/b testing.

Learn how test driven development acts as a shift left, starting from a story that meets the definition of ready, writing failing tests first, building code, then refactoring with CI/CD.

Learn the modern practice of testing on feature branches with dedicated qa environments, ensuring unit and integration tests pass before merging to master and enabling safe, instant releases.

Create on-demand environments from trunk, release, or feature branches by building, testing, and deploying the app and infrastructure, then decommission or scale when idle to save costs.

Explain how a given-when-then BDD framework with Gherkin enables NLP-driven automated testing that mirrors real user behavior, while analytics and user journeys reveal usage patterns and risk to guide tests.

Discover what ai agents are and how they orchestrate prompts, llms, and tools to complete tasks, using prompt chaining, workflow routing, and feedback loops with databases and APIs.

Watch a live demo of an AI agent that automatically creates tests from a story, generating acceptance tests, BDD scenarios, and selenium test cases with flow wise.

Install Flowise locally following the official docs, ensure Node.js and npm are ready, then launch on localhost:3000 to access the agent marketplace and human-in-the-loop tools.

Build and orchestrate an AI agent using prompt chaining, a supervisor, and worker nodes, wired through a ChatGPT wrapper to generate titles and stories.

Demonstrates infrastructure scalability testing through horizontal and vertical scaling with load balancers, threshold-based rules, and cloud auto-scaling to maintain performance during peak traffic.

Demonstrate fault-tolerant architecture with dual data centers, two nodes each, and load balancers distributing traffic to 25% per node, using Azure Chaos Studio for targeted chaos experiments.