AI for software engineering learning guides

Learn how to create a lightweight, project-aware AI agent using open-source RAG techniques. Includes an n8n workflow and example integration with your repo and internal docs.

Explores adding chat-style AI assistants directly into IDEs and internal dashboards. Covers use cases like context-aware help, legacy code Q&A, and codebase explorability. Includes sample integrations using open-source LLMs and embedding agents.

Master the art of collaborative debugging with AI assistants. Learn advanced conversation techniques for walking AI through complex bugs, sharing context effectively, and using AI to generate targeted debugging strategies for production incidents.

Learn to coordinate multiple AI agents for large-scale refactoring projects—one for architecture analysis, another for code migration, and a third for test validation. Includes practical examples with Autogen and CrewAI.

Learn how to create AI debugging assistants that understand your specific codebase's common failure patterns. Covers fine-tuning approaches, creating custom error databases, and building AI workflows that can predict and prevent recurring bugs before they reach production.

A unified agentic reinforcement‑learning loop for goal‑driven reasoning and tool useWalks through building and training an ARTIST agent where reasoning, tool execution (including dynamic tool integration), and multi‑turn planning are jointly optimized. Covers training code, reward shaping, error correction heuristics, and deployment patterns in real‑world task bots.

Use deep learning to choose which agent handles which task in real timeExplains MetaOrch—a neural orchestration layer trained to predict which agent (from a catalog) is best for each subtask based on context embeddings, past performance, and confidence scoring. Includes supervised training setup, fuzzy‑logic evaluator, and plug‑in to LangChain frameworks.

Use tracing, reinforcement evaluators, and guardrails for production reliabilityShows how to apply Azure’s built‑in AgentOps features—tracers, evaluators, AI‑Red‑Teaming agents, and prompt‑firewall guardrails—to monitor reasoning paths, split task retries lazily, escalate after failures, and audit rollout defense chains. Best practices included for deploying with Azure Monitor and OpenTelemetry.

Architecting multi‑level agent orchestration structures for modular workflowsCovers building agent hierarchies where mid‑level or top‑level supervisors route to nested sub‑teams, balancing work across domains (e.g. research, writing, code-review), enabling delegation, backoff retries, and dynamic scaling. Offers trace examples and failover logic patterns.

Treat each RAG module as a cooperative reinforcement‑learning agent for end‑to‑end fine‑tuningIntroduces MMOA‑RAG, a cooperative MARL framework where Document‑Selector, Re‑ranker, Prompt‑Refiner, and Answer‑Generator agents jointly learn via policy gradient to maximize a unified reward (e.g. F1/precise response). Includes code snippet, training loop, and evaluation on benchmark QA sets.

How to architect agent crews that plan, code, test, and self‑heal CI/CD automationExplains how to build multi‑agent DevOps pipelines where ‘Planner’ decomposes PR goals, ‘Coder’ writes patches, ‘Rollback Sentinel’ monitors metrics, and ‘FinOps Optimizer’ enforces budget policies. Includes tooling registry design, zero‑trust YAML guards, vector memory, and ROI benchmarking.

Build interoperable agents across cloud and vendor boundaries using A2A + MCPStep‑by‑step guide to implementing the open A2A Protocol (JSON‑RPC 2.0) for agent collaboration. Covers Agent Card discovery, task negotiation, streaming/push semantics, security scopes, and integration with Microsoft Copilot Studio via Model Context Protocol (MCP). Includes Python and TypeScript SDK examples.

How to design inter‑agent workflows in Microsoft’s latest Semantic Kernel + AutoGen stackA hands‑on guide showing how Azure AI Foundry now uses Semantic Kernel under the hood to enable multi‑agent workflows—supporting stateful routing, agent‑to‑agent calls, fault tolerance, and long‑running orchestration. Covers coordinator agents, error strategies, and building stateful pipelines.

Configuring connected agents for releases, feature‑branch pipelines, and auto‑rollbackA walkthrough showing how to deploy connected agents in Azure AI Foundry Agent Service to automate release pipelines—agents automatically triage PRs, handle test suite generation, trigger environments, observe SLOs, and coordinate rollbacks. Fully integrated with identity, AgentOps tracing, and GitHub CI/CD.

Graph‑based agent teams with supervisor orchestrators in Python & TypeScriptWalk through building hierarchical agent teams using LangChain’s LangGraph Supervisor—covering supervisor‑worker splits, tool injection, feedback loops, adaptive prompting, and versioned graph replay. Includes real‑world workflow examples from research bots to document‑analysis agents.

Prompt patterns and issue templates that improve completion: crisp scope, repo path hints, acceptance tests, and “exit criteria.” Include 5 copy-paste templates (bug, refactor, dependency bump, doc fix, test addition).

Learn advanced techniques for using AI assistants to debug production issues in real-time. Covers prompt strategies for error analysis, log interpretation, and generating hotfixes while maintaining code quality standards.

Use AI to analyze monolithic codebases and identify optimal service boundaries. Learn prompt strategies for dependency analysis, data flow mapping, and generating migration roadmaps that minimize business disruption during decomposition.

Instrument runs with logs/telemetry, capture prompt+tool traces, and measure PR quality (tests added, diff churn, revert rate). Provide a lightweight dashboard (OpenTelemetry + a CSV budget) to keep spend and drift in check.

How to run recurring agent tasks (dead code removal, rename plans, test upgrades) on a schedule. Includes a sample backlog template (“agentable tasks”) and a 30-day rollout with guardrails for maximum diff size and test thresholds.

Define success the easy way: task-level KPIs (exact match, pass@k, compile rate), experience KPIs (first-token latency, abandonment), and cost KPIs (tokens/resolved). Build a small but durable golden set and run pre-merge evals to prevent quality drift.

Hands-on setup of repo-level controls so background agents can’t bypass human review. Walk through CODEOWNERS patterns, required checks, least-privilege tokens, and automations that halt risky changes.

Master the art of using AI to generate comprehensive test suites from natural language specifications, then having AI implement the code to pass those tests—flipping traditional TDD on its head.

When a task is bigger than one PR: model multi-step flows with retries, compensation, and human approvals. Provide architecture diagrams and minimal examples for each orchestrator.

A safe workflow for PMs/marketers: open a well-scoped issue in plain language, let the agent produce a draft PR, then request owner review. Tight scope, zero local setup, auditable changes.

Design a simple experiment: compare agent vs. human throughput on a curated issue set, track PR cycle time and defect escape. Borrow ideas from public coding-agent benchmarks, but tailor them to your stack.

Wire an agent into CI to attempt fixes after failing tests or vulnerability scans. Add staged checkpoints (triage → draft patch → gated PR) so humans approve at key moments.

Spin up ephemeral dev environments (e.g., devcontainers) so agents run tests and linters in isolation. Show how to inject only the minimum secrets, scrub prod data, and snapshot artifacts for audit.