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.
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 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
Curriculum
Phase 1: 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.
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 2: 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.
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.
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.
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 3: 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.
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.
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.
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 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
ReAct demos work in a notebook… but unsupervised agents in production fail loudly.
Without the full agent stack, you'll hit:
- Tool calls that succeed-but-wrong, with no schema validation to catch it
- Memory that grows unbounded until token budgets blow up
- Multi-agent loops that deadlock or run up cost in the background
- Agents that take irreversible actions because there's no HITL gate
- An agent service you can't debug — no traces, no profiling, no evals
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.
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.
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
AGENTIC vs alternatives
AGENTIC vs Chatbots
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.
AGENTIC vs Workflow 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.
AGENTIC vs RAG 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.
Related skills
- LLM Pipeline Engineering — Agents build on LLM pipeline patterns from
- Enterprise AI — Production agent safety connects to enterprise AI practices in
- LLM Evaluation — Agent quality is measured using evaluation frameworks from
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.
Common questions about AGENTIC
What are agentic AI systems?
Agentic AI systems use LLMs to autonomously plan and execute multi-step tasks. They call tools, manage memory, and make decisions to complete complex workflows without human intervention.
Are AI agents production-ready in 2026?
AI agents are increasingly deployed in production for specific workflows. They require careful guardrails, monitoring, and human oversight. Fully autonomous general-purpose agents are still maturing.
How long does it take to learn agentic AI?
Basic agent patterns take 2-3 weeks. Building production agents with tool use, multi-agent orchestration, observability, and HITL takes 6-8 weeks of hands-on practice.
What is multi-agent orchestration?
Multi-agent orchestration coordinates multiple specialized agents working together. The supervisor pattern (one orchestrator, N workers) is the most common production topology — easier to debug than peer-to-peer agent networks.
What is HITL in an agent system?
Human-in-the-loop (HITL) is the approval gate where an agent pauses before an irreversible action — a destructive SQL write, an external API call, a transaction — and asks a human via Slack, email, or a queue. Required infrastructure for any agent that runs against production systems.
Do data engineers need agentic AI skills?
Agentic AI skills are valuable for engineers building AI applications, especially when agents run inside data pipelines (agentic ETL). The infrastructure — tool integration, memory, observability, evaluation — builds directly on data engineering patterns.
What frameworks are used for AI agents?
LangGraph for stateful agent graphs, CrewAI / AutoGen for multi-agent role specialization, and Anthropic / OpenAI function-calling APIs for tool use. Most production systems use a thin custom orchestrator on top, not pure framework.
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