Claude Agentic AI in Practice Certification Course

Explore system prompts as operating contracts for model behavior, connect to architecture decisions, separate durable rules from per-task context, and test behavior under conflicting input.

Structure complex prompts with semantic boundaries using XML to separate meaning and JSON schema to constrain output. Apply this approach to enterprise design and practical exercises.

Control reasoning and output formats by specifying decision criteria and exposing only the necessary evidence and results. Design uncertainty and refusal outputs to support auditable, constrained architecture decisions.

Learn to treat prompts as production code, using a disciplined evaluation and iteration loop with dataset, rubric, runner, and review to ensure measurable, versioned, and safe deployments.

Explore tool use fundamentals in Claude by defining the tool use contract, implementing the client tool loop, and handling stop reasons and tool errors to design reliable, enterprise-ready tool interactions.

Design tool schemas and APIs by building narrow, verifiable interfaces that separate read, preview, and commit actions, with intent-rich names, bounded effects, and verifiable results.

Select tools by epistemic job to retrieve facts, compute deterministically, or transact with a system of record; normalize results for code and preserve provenance and error semantics for architecture decisions.

Build reliable tool invocation patterns by applying schema validation, authorization, idempotency, timeouts, and observable outcomes. Design defensive tool dispatcher and retry strategies through a hands-on enterprise scenario and design exercise.

Rag architecture fundamentals show how a language task becomes an evidence pipeline through ingest, retrieve, assemble, answer, and verify with citations.

Design retrieval systems by chunking data, creating embeddings, and using vector stores. Understand how chunk boundaries, embeddings, and metadata shape retrieval quality through practical design exercises.

Assemble a cloud rag pipeline that passes compact evidence with stable source ids and an explicit answer contract, preserving citations and handling conflicting evidence.

Evaluate retrieval quality by separating retrieval from answer generation, build retrieval test sets, and measure recall, precision, and ranking to connect metrics to failures in architecture.

Differentiate short-term and long-term memory and when to retain facts. Apply an enterprise memory design with context, session state, durable memory, and clear expiry and correction paths.

Explore session state and persistence strategies by modeling explicit agent state and durability checkpoints. Learn to resume work with tokens to prevent duplicate actions and maintain explicit data.

Vector memory retrieves by similarity, and symbolic memory retrieves by exact meaning and business keys. Design hybrid retrieval with clear provenance, TTL, and memory policy to prevent contamination.

Design and implement memory read and write gates, trust checks, and user-visible memory controls in a memory-aware agent, balancing relevance, policy, and consent.

Explore planning and reasoning loops by modeling a bounded agent as a state machine with budgets and terminal states, defining observe, decide, act, and stop steps for reliable, auditable control.

Decompose work into independently verifiable units with explicit dependencies and outputs, choosing sequential, parallel, hierarchical, or adaptive decomposition. Define verifiable subtask contracts and evidence to guide architecture decisions.

Classify agent and tool failures, design safe retry with bounded exponential back off, repair invalid inputs, reconcile ambiguous effects, and escalate unknowns, preserving evidence across recovery.

Design observability for an agent by creating a trace, separating logs, metrics, and audits, and safeguarding sensitive telemetry in a distributed workflow.

Define narrow agent roles and collaboration patterns to improve system architecture; implement specialist, reviewer, router, and synthesizer with typed handoffs, jurisdiction-specific rules, and verified provenance to measure overhead.

Compare supervisor–worker and swarm models, learning to choose coordination topology by risk and embed verification and budget controls in enterprise workflows.

Design agent communication protocols with a typed task-evidence-state envelope and preserved provenance across handoffs. Choose direct, queued, or shared state, using durable messaging and replay, deduplication and versioning.

Classify multi-agent conflicts, design deterministic adjudication, and prevent livelock through evidence-based policy and authority rules in practical design exercises.

Compare LangChain, Lang graph, and Lama index for Claude with custom frameworks; decide build versus adopt and preserve provider boundaries to design reliable, enterprise-ready agents.

Explore workflow orchestration versus autonomous agents, using deterministic orchestration for known control flow and model autonomy where judgment changes path; design a hybrid architecture with map, locate, bound, and gate.

Model event-driven agent systems by defining events, consumers, durable state stores, and an orchestrator; enable idempotent handling, retries, and explicit ordering for long-running workflows.

Compare orchestration approaches using a weighted decision model and a working spike to inform an architecture decision in enterprise scenarios, supported by a practical design exercise and evidence-driven trade-offs.

Design a human-led copilot for business workflows by integrating context, evidence-linked recommendations, reversible drafts, and accountable approvals from observe to learn.

Automate customer support by confidence and consequence, using self-service, triage, agent-assist, and action with policy gates to ground decisions, escalate when needed, and enable durable resolution.

Design an access-aware knowledge assistant for internal teams that surfaces freshness, ownership, and verifiable evidence, aligning identity, corpus, evidence, and feedback to support trust and answerability.

Explore how AI agents integrate into software engineering by enforcing tight feedback through repositories, tests, permissions, and review, and learn to design safe access, tests, and controls for reliable automation.

Apply guardrails as a layered control system across input, model, tools, output, and operations. Build an agent risk register, map controls to failure modes, and define residual risk with owners.

Assess prompt injection and data leakage risks by treating untrusted input as adversarial data, designing a trust hierarchy, enforcing least privilege, and limiting data exposure across inputs, tools, and outputs.

Design an audit event model that captures intent, evidence, policy, action, and outcome with an accountable owner. Capture evidence without hidden reasoning and enable reconstruction and review for enterprise systems.

Explore how agentic systems inherit existing control obligations and require new evidence, access, and change management within enterprise controls, aligning architecture decisions with practical design and testing for compliance.