Your First AI Agent
Define what an agent is (vs script, vs hosted-API call), build the smallest working ReAct loop, and watch the failure modes — runaway tool calls, lost context, hallucinated outputs — that motivate every later pillar.
Phases
3
AI SystemPhase 1 in ProfessionalFull access in Expert
Agentic AI Systems
Build agents that reason, call tools, manage memory, and run real data work — from supervisor patterns to agentic ETL and observability.
Most agent demos are toy ReAct loops. The teams shipping real agents — supervisor topologies, durable retries, HITL approvals, and agents that actually run inside data pipelines — own a wedge of automation that wasn't possible 18 months ago.
What you'll do
What you'll be able to do.
- Build single-agent loops with structured tool calls and memory
- Design supervisor / multi-agent topologies with durable state and HITL
- Run agents inside data pipelines (agentic ETL with self-healing decisions)
- Profile, observe, and evaluate agent behavior in production
Phase roadmap.
Phase 1PRO REQUIRED
Agent Foundations
What an agent actually is — vs a script, vs an API call. The reasoning loop, the tool interface, the failure modes that turn a demo into an outage.
1.1
⊘Your First AI Agent
Define what an agent is (vs script, vs hosted-API call), build the smallest working ReAct loop, and watch the failure modes — runaway tool calls, lost context, hallucinated outputs — that motivate every later pillar.
1.2
⊘Agent Architecture
LLM-as-reasoning-engine: planning vs acting, structured outputs, the control loop that turns a model into an agent, and the architectural choices (ReAct, plan-and-execute, code-as-policy) that decide whether your agent is debuggable.
Phase 2EXPERT REQUIRED
Agent Capabilities
The three things that move an agent from prototype to production: tools you can trust, memory that survives restarts, and a multi-agent topology that doesn't deadlock.
2.1
⊘Tool Use & Function Calling
Tool standards (JSON Schema, OpenAPI), schema-validated I/O, retry semantics, idempotency keys, and the RBAC tool registry pattern that keeps an agent from doing something it wasn't authorized to do.
2.2
⊘Memory & Context Management
Short-term scratchpad vs long-term memory, vector store + summarization patterns, durable checkpointing (Redis / Postgres), time-travel for debugging, and the eviction rules that stop context from blowing up your token budget.
2.3
⊘Multi-Agent Orchestration
Supervisor pattern, role-specialized worker agents, shared blackboard memory, hand-off protocols, and the deadlock + cost-loop failure modes you only see when two agents talk to each other.
Phase 3EXPERT REQUIRED
Agentic Data Workflows
Agents that do real work inside data pipelines. Decision-making ETL, profiling that the agent reads and reasons over, HITL approval gates, and the observability stack that keeps an agent service alive on-call.
3.1
⊘Agentic ETL
Agents that orchestrate ETL — decision points where an agent picks transforms, self-healing pipelines, traditional ETL vs agentic ETL tradeoffs, and the HITL pattern that keeps an autonomous pipeline from doing something irreversible.
3.2
⊘Agent Observability & Profiling
AI profiling strategy where data quality is something the agent reads and acts on, trace + span instrumentation for tool calls, cost-per-decision tracking, eval-in-production patterns, and the on-call runbook for an agent service that hangs.
3.3
⊘Agentic AI Capstone
End-to-end build: define a real automation problem, design the agent topology, implement tool registry + memory + HITL, ship it under observability, and defend the design choices in an architecture review.
What you'll ship
What you'll build.
- Tool-calling agent with JSON-Schema-validated I/O and retry logic
- Supervisor multi-agent system with shared memory + HITL approval
- Agentic ETL pipeline that reads profiling, makes decisions, self-heals
- Agent observability stack: trace, profile, eval, cost dashboard
Definition
What is Agentic AI Systems?
Agentic AI systems are autonomous programs that use LLMs to reason, plan, and execute multi-step tasks by calling tools and managing memory. Unlike simple chatbots, agents can browse the web, write code, query databases, and orchestrate complex workflows. Companies like Anthropic, OpenAI, and Salesforce are building agent platforms that automate knowledge work.
Production context
Why this matters in production.
Agentic AI is the fastest-growing AI application pattern, but the gap between an agent demo and an agent in production is wide. Real deployments require tool registries with RBAC, durable memory and checkpointing, HITL approval gates, and observability for a system whose behavior is non-deterministic by design. Without those, agents take harmful actions, leak data, or run up cost in the background.
Use cases
Common use cases.
- Building tool-calling agents with schema-validated I/O and retry logic
- Designing supervisor / multi-agent topologies with role specialization
- Implementing durable agent memory with checkpointing and time-travel debugging
- Running agents inside data pipelines for self-healing, decision-making ETL
- Adding HITL approval gates and audit trails to autonomous workflows
- Profiling, tracing, and evaluating agent behavior in production
Compare
Agentic vs alternatives.
AgenticvsChatbots
Agents take autonomous actions and use tools. Chatbots respond to messages in a conversation. Agents can plan multi-step workflows; chatbots handle single-turn or simple multi-turn interactions.
AgenticvsWorkflow Automation
Agentic systems use LLM reasoning for dynamic decisions. Traditional automation follows predetermined rules. Agents handle ambiguous, novel situations; automation handles repetitive, well-defined tasks.
AgenticvsRAG Systems
Agents take actions and use tools autonomously. RAG retrieves information to augment LLM responses. Agents often use RAG as one tool among many in their toolkit.
Why this matters
Why this skill matters.
Agentic AI is the in-demand AI capability of 2026. This skill proves you can move past chatbot demos into agents that hold state, call tools safely, and run real workloads — the difference between an AI prototype and an AI platform.
FAQ
Common questions about Agentic.
Agentic AI SystemsUpgrade to Professional