GenAI Engineering: Agents & Mastery

• Format — 4,300+ MCQs with instant explanations
• Duration — about 47.9 hours of focused practice, typically 10–15 weeks at 40–60 questions a day
• Level — beginner to advanced; LLM Foundations and Prompt Engineering recommended first
• Cost — free, with per-module and series completion certificates
• Audience — engineers building tool-using systems, multi-agent architects, AI Engineer interview candidates, builders moving from single-prompt LLM apps to autonomous workflows
• Previous modules in the track — LLM Foundations, Prompt Engineering, RAG Systems

Three audiences land on this page. Working software engineers and data scientists who have shipped chatbots or copilots and are now being asked to build agents — multi-step tool callers, autonomous workflows, coding copilots, browser agents. The gap between a LangChain demo and a reliable agent is wider than any tutorial admits, and this course covers it. Final-year engineering and MCA students targeting AI Engineer interviews in 2026, where "design an agent for X" is now a standard whiteboard question and the bar is concrete pattern fluency — when to use ReAct vs plan-and-execute, when multi-agent beats single-agent, how to defend against indirect prompt injection through tool output. Builders who've used CrewAI, LangGraph, or AutoGen tutorials and now want first-principles grounding in why agents fail at scale — failure modes the libraries hide behind abstractions.

Foundations

• What Is an AI Agent — defining an agent (agent vs chatbot, perception-action loop, autonomy levels, agent vs workflow); why agents now (tool-calling capability emergence, longer reasoning chains, structured output reliability, API ecosystems and connectors); the spectrum from static workflow through router agent to fully autonomous and multi-agent
• Tool Use Fundamentals — tool definition (name and description, JSON schema for arguments, return type, side-effect vs read-only); function-calling mechanics (API format, parallel calls, tool_choice parameter, forced tool use); tool-result handling (tool message role, error formatting, large-output truncation, binary and file outputs)
• Agent Reasoning Patterns — the ReAct loop (cycle state tracking, reasoning before action, observation parsing failures, max-step and stuck detection); plan-and-execute (upfront plan generation, in-flight plan revision, plan-as-todo-list, plan vs reactive tradeoff); reflexion and self-critique (self-evaluation step, memory of past failures, retry with critique, confidence-threshold stopping)

Architecture

• Memory Systems — short-term memory (conversation history, scratchpad, context window as memory, session memory truncation); long-term memory (vector memory store, summary memory, key-value fact memory, retrieval on demand); memory management (writing decisions, consolidation, stale-memory eviction, conflict resolution)
• Planning and Decomposition — task decomposition (subgoal identification, dependency graphs, least-to-most decomposition, recursive); search-based planning (tree of thoughts, Monte Carlo tree search for agents, branch pruning by critic, rollout cost control); plan repair (replanning on failure, partial vs full replan, human-in-the-loop on stuck plans, graceful degradation)
• Multi Agent Systems — agent roles (planner-executor, writer-critic multi-agent role split, manager-worker hierarchy, agent debate as decision protocol); communication patterns (direct message passing, shared scratchpad, broadcast-subscribe, structured message schemas); coordination failures (infinite agent loops, conflicting decisions, redundant tool calls across agents, deadlock in dependencies)
• Tool Ecosystems and Protocols — common tool categories (search and retrieval, code execution, file system, HTTP and API, database query); protocols and standards (MCP, OpenAI function-calling schema, JSON-RPC tool servers, tool registry patterns, A2A agent-to-agent protocol); sandbox isolation (code execution sandboxing, network egress restrictions, file-system jailing, resource quotas)

Production and Interview readiness

• Reliability and Control — loop prevention (max iteration limits, no-progress detection, duplicate-action detection, cost budget cutoffs); human-in-the-loop (approval before destructive action, ambiguity escalation, diff preview before commit, confirmation prompts); determinism (seed and temperature control, tool-call logging, replayable traces, state checkpointing)
• Evaluation of Agents — task success metrics (end-to-end success rate, partial credit scoring, step efficiency, tool-call accuracy); agent benchmarks (SWE-bench for code agents, WebArena for browsing, AgentBench general, tau-bench for tool use); failure analysis (trace-level error categorization, hallucinated tools at runtime, premature termination, trace-level loop diagnosis)
• Safety and Permissions — authorization model (least-privilege tool access, scoped credentials per tool, user-delegated permissions, per-session token isolation); prompt injection in agents (indirect injection in retrieved documents, tool-output injection at runtime, exfiltration via outbound tools, untrusted-content quarantine); auditing and accountability (action log retention, user attribution, rollback capability, post-hoc review workflow)
• Production Agent Patterns — cost control (model tiering by step, tool-result caching, early-exit conditions, per-task budgets); latency optimization (parallel tool dispatch at scale, streaming intermediate output, speculative tool execution, small-model routing); observability (trace timeline reconstruction, span-level latency, token accounting per step, alerting on cost spikes)
• AI Agents Mastery — pattern comparisons (ReAct agent vs plan-and-execute agent, single-agent vs multi-agent, tool agent vs code-interpreter agent, static workflow vs autonomous); when-to-use selection (workflow vs agent, critic agent, code execution tool, human approval, multi-agent); agents in practice (customer support triage, coding assistant, research synthesis, data-analysis SQL, browser automation)

RAG Systems teaches retrieval-augmented generation — fetching relevant context at query time and grounding the LLM response in it. Agents teaches tool-using autonomous systems — the LLM decides what action to take, executes it, observes results, and iterates. The two are coordinate, not sequential. Many production systems use both — an agent that retrieves (RAG) as one of its tools, or a RAG system whose retriever is itself agentic (corrective RAG, self-RAG). The boundary between "advanced RAG" and "simple agent" in 2026 is mostly cosmetic — agentic retrieval, iterative retrieval with reasoning, self-RAG with critique all live on the boundary. Take whichever fits your immediate use case first; most learners do both over the four-module track.

Most agent systems that reach production hit a small set of recurring anti-patterns. This course is designed to surface and correct them through targeted MCQs. Among the most common:

• Agents are not always better than workflows — for deterministic flows, a static workflow with a few LLM calls is more reliable, cheaper, and easier to debug.
• More tools doesn't mean a better agent — tool selection accuracy drops as the tool count grows, and tool descriptions compete for context-window real estate.
• A ReAct loop without max-step limits is a runaway cost incident waiting to happen — the agent will keep reasoning until either you intervene or the bill arrives.
• "Just add a critic agent" doesn't always improve quality — critic agents can sycophantically agree with bad outputs or over-criticise good ones, producing different failure modes than the original agent.
• Multi-agent systems are not free — every additional agent multiplies coordination overhead, failure surface, and trace complexity; debug effort scales worse than linearly.
• Function-calling APIs hallucinate too — argument types, tool names, even tools that don't exist in the schema. Don't trust the API to be infallible.
• Long-term memory eats vector-DB cost — agents that write everything to memory bloat the index and slow retrieval; memory writing must be selective.
• Determinism is not "set temperature 0" — agents need explicit state checkpointing, seed control, and replayable traces; sampling parameters alone don't get you there.
• Indirect prompt injection via tool output is the dominant 2026 attack surface for agents — not the dramatic direct jailbreaks. If your agent fetches anything user-influenced, you have an injection problem.
• SWE-bench scores don't translate to your codebase — public benchmarks are heavily optimised against, and real-world agent reliability lags the headline numbers significantly.

Each of these has a dedicated cluster of practice questions in the curriculum.

Build something complex, watch it fail in surprising ways, then learn the patterns that name the failure. Most engineers who learn agents from CrewAI or LangGraph tutorials ship demos that work on the happy path and silently degrade on real inputs. Answering 4,300+ specific MCQs — with the explanation appearing immediately after every wrong answer — forces retrieval practice on the failure surface that tutorials skip. The Abekus AI guide tracks which subtopics you keep missing and re-surfaces them later. About 47.9 hours of focused practice covers the full curriculum end to end.

One question at a time. Pick an answer. If you are wrong, the explanation appears immediately — usually a paragraph that walks through the failure mode (why does this ReAct loop fail to terminate? why does this multi-agent system deadlock? why does this tool description hallucinate?), names the concept by its standard term, and points at the related labels. The AI guide watches your accuracy by subtopic and prioritises the weak ones in the next session. There is no video, no scrolling lecture — just focused retrieval practice on the patterns that matter for production agents.

The honest math: 4,300+ MCQs at about 40 seconds each (including reading the explanation on the wrong ones) is roughly 47.9 hours of pure focus. Spread that over 40–60 questions a day and you finish in about 10–15 weeks. At 80 questions a day, about 8 weeks. The course is designed for short ad-hoc sessions of 20–30 minutes — you do not need to block off a weekend. Most learners who reach this module have already spent 8–10 weeks on the prior three modules; finishing here completes the four-module Generative AI Engineering track.

The mastery topic mirrors the agent system-design questions that actually get asked in AI Engineer technical screens in 2026 — "design a customer-support triage agent with escalation", "walk me through how you'd debug an agent that loops forever", "explain when multi-agent beats single-agent for X". The Safety and Permissions topic prepares you for the security round (indirect injection via tool output, least-privilege tool access, untrusted-content quarantine). The Evaluation topic prepares you for the eval round (SWE-bench gaming, trace-level failure analysis, hallucinated-tool detection). The Production Agent Patterns topic prepares you for the "how would you scale this" round (cost tiering, parallel dispatch, observability).

Completing this module finishes the four-module Generative AI Engineering track and unlocks the series certificate — the credential designed to be credible for AI Engineer applications, significantly more substantive than the per-module certificates because it confirms coverage of the full GenAI stack (LLMs, prompting, RAG, agents). The series certificate has a public verification URL you can include on LinkedIn or in a resume. Independently of the GenAI track, learners targeting Data Scientist or ML Engineer roles typically follow this with the Machine Learning Foundations, Statistics for Data Science, and Data Science Interview Mastery courses on Abekus.