1. Intelligence — The Brain
Powered by an LLM (GPT, Claude, Gemini). Handles reasoning, planning, language understanding and task decomposition.
Why attend?
Whether you're a scrappy entrepreneur, ambitious marketer, creator, or curious professional — this workshop guides you through building a practical, production-ready AI Agent. By the end you'll have a working agent and the architecture to scale it safely.
What you'll learn
A concise tour from vision to architecture to launch
- Agent Foundations Playbook: The 4 pillars — Intelligence, Integrations, Instructions, Memory.
- Agentic Loop: Perceive → Plan → Act → Observe in practice.
- Hands-on: Build an agent that connects to real tools and a RAG memory store.
- Deployment: Governance, observability, and safe HITL patterns for production.
Join the Workshop
Hands-on • Limited seats
Agent Foundations Playbook — The 4 Pillars
Every true AI agent needs all four pillars to be functional, reliable, and actionable.
2. Integrations — Hands & Feet
APIs and tools that let the agent act: Slack, Google Sheets, CRMs, email, browsing, or orchestrators like n8n.
3. Instructions — The DNA
The system prompt, role-based prompting, constraints and governance that define behavior and reliability.
4. Memory — Context & Learning
Short-term context windows and long-term stores (RAG/vector stores) that persist knowledge and prevent hallucinations.
Insight: Focusing on Intelligence alone leads to brittle systems. True agents need the full stack—like a car needs all four wheels to move reliably.
Deep dive: Integrations — the agent's Actuators
The Integrations pillar transforms planning into real-world action. It defines the agent's sphere of influence.
Integrations are tools and actuators. They let an agent execute an action (send emails, update CRMs, query databases). The most important practical aspects are:
- Tool Types: Data tools (search, DB queries), Action tools (write, update, transact), Orchestration tools (invoke sub-agents), and Resources (readable context objects).
- Design principle: Define tools with precise names, parameters, and concise descriptions so the LLM picks them reliably.
- MCP: Use a standard like the Model Context Protocol to decouple model logic from tool security and implementation.
- Platforms: Integration platforms range from no-code (Zapier, n8n) to code-first SDKs (LangChain, Claude SDK, AgentKit).
Practical tip: Model selection + properly scoped tools = predictable behavior and lower cost. Treat tools as first-class primitives when designing an agent.
Foundations & Core Concepts
From agentic loop to architecture blueprints — what separates an agent from a chatbot.
Agentic Loop
Perception → Planning/Reasoning → Action → Observation. Each pillar maps to a phase of the loop: Memory senses, Intelligence plans, Integrations act, Instructions constrain.
Architectural patterns
- ReAct & CoT: Explicit thought traces and tool calls for transparency and performance.
- Hierarchical planning: Supervisor → worker agents to avoid context overload and increase specialization.
- PEAS: Performance, Environment, Actuators, Sensors — use it to define success and scope.
Advanced Agentic Engineering
Engineering agents is an architectural discipline: orchestration, governance, and reliability at scale.
Multi-Agent Systems
Specialization is key. Break large goals into worker agents (Researcher, Writer, Critic). Use hierarchical or parallel patterns depending on latency and complexity.
Orchestration & Protocols
Use MCP for tool standardization and emerging A2A protocols for agent-to-agent communication. Orchestration frameworks map roles, messages and telemetry.
Reliability & Governance
HITL gates, guardrails, observability (traces, logs, evals) and safe-failure design reduce risk in production.
Context Engineering
Persist, summarize, and scope context (RAG, vector stores) to manage costs and prevent prompt fragility.
Platform & Framework Comparison — Choose with intent
There is a trade-off between speed (no-code) and control (code-first). Match the platform to your goals and scale needs.
| Category | Examples | When to use |
|---|---|---|
| No-code / Orchestration | n8n, Zapier, Make.com, OpenAI Agent Builder | Rapid prototyping, visual flows, non-technical automation. n8n for control/self-hosting; Zapier for breadth. |
| Code-First Frameworks | LangChain, Claude Agent SDK, CrewAI, AutoGen | Deep customization, multi-agent orchestration, autonomous coding, and production integration. |
| MCP & Standards | Rube.app, MCP servers | Standardize tool exposure, keep secrets server-side, ship portable agents across LLMs. |
Strategic guidance: start with a visual prototype and move code-out for production, or choose self-hosted n8n for cost control and data sovereignty.
Building & Deployment Best Practices
From design to CI/CD — pragmatic rules engineers use to deploy safe, observable agents.
Design first
- Start small. Narrow scope and define success metrics up front.
- Use PEAS and a brief PRD to formally specify goals, environment, sensors, and actuators.
- Modularize: many specialized agents beat one monolith.
Implementation rules
- Engineer system prompts like governance docs; iterate and test for prompt fragility.
- Implement self‑critique loops and validation checks (critic agents).
- Design tools with strict schemas and clear parameterization; minimize ambiguity.
Operational / EvalOps
- Human-in-the-loop for irreversible actions; set confidence thresholds and approval gates.
- Observability: logs, traces, and standardized telemetry are essential for debugging multi-agent flows.
- Cost engineering: match models to roles; prefer cheaper models for simple workers and reserve high-cost models for planning.
Practical Applications & Use Cases
Real-world impact across enterprise, productivity and creative workflows.
Enterprise
Customer triage, contract intelligence, lead enrichment, automated auditing — agents reduce time-to-value and scale specialist work.
Personal Productivity
Autonomous schedulers, email triage, research assistants and travel planners that execute multi-step workflows on your behalf.
Developer & Ops
Code generation + review agents, CI helpers, infra automation and multi-agent systems that build and test software components.
Speculative
Autonomous businesses and meta-agents that design and spin up new agents—future directions that reward engineering rigor today.
Capstone ideas: Personal Research Assistant, Company Policy RAG bot, Multi-agent Research Team, Trip Planner with live APIs.
Next steps — Join the workshop
Reserve a seat, bring a concrete use case, and leave with an agent you can iterate on.
Reserve your spot
Resources
Preview: luma.com/tsz6398v
Event page: cuculi.net