
Explore how agentic systems differ from workflows and chatbots by detailing the four agency pillars—perception, selection, execution, and iteration—and the dynamic agentic loop that governs tool use and termination.
Decompose goals into well-defined subtasks using four criteria and three patterns to prevent agentic failures. Design clear handoffs with explicit output schemas and minimal payload.
Learn how the orchestrator–sub-agent model enforces context isolation, bounded scope, and explicit handoffs to build auditable, scalable agentic systems from plan to execution.
Explore hook lifecycle events and the mix of programmatic and prompt-based guardrails that enforce workflows, with pre-action, post-action, and on-error hooks for interception, validation, escalation, and schema checks.
Explore how the MCP client discovers servers, routes requests, and injects application-controlled resources into the model’s context across multi-server setups, while prompt templates remain user-controlled.
Explore transport options in MCP, including STDIO and streamable HTTP, with SSE progress, sampling, roots, and robust security through schema, server-side authorization, and audit logging.
Explore chain of thought prompting, prefilling, and format anchoring to improve accuracy in multi-step tasks, understand their distinct roles, and learn best practices for reliable structured outputs.
Tune parameters by adjusting temperature, top p, or top k one at a time to balance determinism and creativity, and plan context management with summarization or windowing to prevent overflow.
Learn to choose between JSON mode and tool use for structured output, recognizing JSON mode guarantees syntax but not schema, while tool use enforces schema via the function argument contract.
Learn explicit escalation design and error propagation in multi-agent pipelines, including how to classify failures, implement structured handoffs, and distinguish access failures from empty results for robust recovery.
This course contains the use of artificial intelligence. However, every lecture recording involves me reading the scripts, and I am fully involved in scripting and production. Be careful buying courses with instructors that don't appear in person. AI courses are becoming quite common on learning platforms.
This fast-track course is a condensed review and readiness assessment for the Anthropic Claude Certified Architect — Foundations (CCA-F) exam. It covers all exam domains in a focused format, then includes a full Exam Simulation practice test built to the same difficulty and domain weighting as the real exam. Use it to review efficiently and immediately assess whether you're ready to sit — or where you need to go deeper.
D1 — Agentic Architecture & Orchestration (27% of the exam) — covers defining agentic systems — autonomy, tool use, and the action loop, agents vs. workflows vs. conversational systems, when to use agentic architecture vs. simpler patterns, task analysis and decomposition strategies, sequential vs. parallel execution patterns, dynamic planning, replanning, and ambiguity handling, orchestrator-subagent model — roles, scope, and context isolation, multi-agent topology patterns — hub-and-spoke, pipeline, peer-to-peer, agent communication, handoff schemas, and error propagation, state and session management — in-context vs. external memory, error classification — tool, reasoning, and environment errors, fallback and retry strategies, programmatic enforcement vs. prompt-based guardrails, human-in-the-loop escalation design. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D2 — Claude Code Configuration & Workflows (20% of the exam) — covers claude code architecture — tool system and execution model, project setup, context management, and session continuity, user, project, and team-level claudemd — hierarchy and precedence, path-specific rules, subdirectory config, and claudeignore, writing effective claudemd instructions for teams, custom slash commands — creation, structure, and distribution, skills — skillmd, frontmatter, triggers, and enterprise deployment, subagents in claude code — configuration, scope, and delegation, hooks — lifecycle events, implementation, and exit code conventions, useful hook patterns — logging, safety nets, and automation, claude code sdk — programmatic session control, ci/cd integration — non-interactive mode and the -p flag. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D3 — Prompt Engineering & Structured Output (20% of the exam) — covers clarity, specificity, and instruction design, system prompts — structure, role definition, and context injection, xml tags and document structure for complex prompts, few-shot prompting and high-quality example design, chain-of-thought and extended thinking, prefilling, output steering, and format anchoring, temperature, top_p, top_k — generation parameter control, multi-turn conversation design and context accumulation, json mode and schema-constrained output, tool use for structured data extraction and validation loops, prompt evaluation — test datasets, grading, and regression testing. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D4 — Tool Design & MCP Integration (18% of the exam) — covers tool descriptions as routing mechanisms, tool input schema design — parameters, enums, and constraints, tool error handling, idempotency, and partial success, mcp architecture — servers, clients, and the three primitives, building mcp servers — defining and exposing tools, resources and prompt templates in mcp, mcp clients — discovery, invocation, and multi-server routing, mcp transport mechanisms — stdio vs. streamablehttp, advanced mcp features — sampling, notifications, and roots, mcp security — access scoping, authentication, and production hardening. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D5 — Context Management & Reliability (15% of the exam) — covers context window architecture — tokens, limits, and cost tradeoffs, the lost in the middle effect — evidence and mitigation, rag architecture — chunking, embedding, and the retrieval pipeline, semantic search, bm25, and hybrid retrieval, multi-index rag, production hardening, and citation, prompt caching — eligibility rules, cache placement, and cost impact, batch api — 50% cost savings, 24-hour window, and workload design, long-conversation coherence, compaction, and session memory. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
The included Exam Simulation is the honest version of readiness testing — scenario-heavy, just like the real exam. If you score well, you're ready. If you don't, the full course is built to close those gaps domain by domain. This course works as a standalone readiness check or as the final step in a complete study plan.
Major topics covered: defining agentic systems — autonomy, tool use, and the action loop, agents vs. workflows vs. conversational systems, when to use agentic architecture vs. simpler patterns, task analysis and decomposition strategies, sequential vs. parallel execution patterns, dynamic planning, replanning, and ambiguity handling, orchestrator-subagent model — roles, scope, and context isolation, multi-agent topology patterns — hub-and-spoke, pipeline, peer-to-peer, agent communication, handoff schemas, and error propagation, state and session management — in-context vs. external memory, error classification — tool, reasoning, and environment errors, fallback and retry strategies, programmatic enforcement vs. prompt-based guardrails, human-in-the-loop escalation design, claude code architecture — tool system and execution model, project setup, context management, and session continuity, user, project, and team-level claudemd — hierarchy and precedence, path-specific rules, subdirectory config, and claudeignore, writing effective claudemd instructions for teams, custom slash commands — creation, structure, and distribution, skills — skillmd, frontmatter, triggers, and enterprise deployment, subagents in claude code — configuration, scope, and delegation, hooks — lifecycle events, implementation, and exit code conventions, useful hook patterns — logging, safety nets, and automation, claude code sdk — programmatic session control, ci/cd integration — non-interactive mode and the -p flag, clarity, specificity, and instruction design, system prompts — structure, role definition, and context injection, xml tags and document structure for complex prompts, few-shot prompting and high-quality example design, CCA-F exam prep 2026.