Papy's Agentic AI Systems Builder

// When should you use Papy's Agentic AI Systems Builder?

Use this skill when you need to architect or evaluate an agentic AI application — any system where a goal is given, broken into tasks, and executed autonomously via tools, memory, and conditional logic. Also use it when deciding whether a task warrants a simple LLM/RAG approach versus a full agentic workflow.

// What do you need before building an agentic AI system?

• User Goal or Taskrequired
  The high-level objective the agent must pursue, stated as a prompt (e.g., 'Hire a remote backend engineer with 2–4 years of experience').
• Available Toolsrequired
  List of external APIs, databases, search engines, calendars, or services the agent may call (e.g., LinkedIn API, Google Calendar, vector database).
• LLM Selectionrequired
  The large language model to serve as the agent's brain — e.g., GPT-4, LLaMA, Mistral, Gemini. Loaded via LangChain.
• Memory Strategyrequired
  Specify short-term memory (current session state) and long-term memory (persistent conversation/database). Define the database or checkpointer to use.
• Human-in-the-Loop Checkpoints
  Define which steps require human approval before the agent proceeds (e.g., before sending an offer letter, before spending on ads).
• Guardrails and Policies
  Hard rules the agent must never violate (e.g., 'Never schedule interviews on weekends', 'Never send offers above budget X').
• Deployment Target
  Where the system will be deployed — AWS, Render, or local Docker. Determines CI/CD and environment variable setup.

// What are the core principles behind building agentic AI systems?

// How do you build an agentic AI system step by step?

1. 1

   Classify the task: LLM, RAG, or Agentic

   Ask: Is the answer already in the LLM's training data (pre-knowledge-cutoff)? If yes → plain LLM call. Is the answer in a static private knowledge base? If yes → RAG (LangChain + vector database). Is the task real-time, multi-step, tool-dependent, or requires autonomous decision-making? If yes → Agentic system. Only proceed to agentic architecture if the task genuinely requires it.

2. 2

   Define the Goal and Constraints

   Translate the user prompt into a structured goal object. Store: goal description, constraints (e.g., experience range, budget, remote-only), start timestamp, and active status. This becomes the agent's core memory seed — without it the agent cannot orient its plan. Use Pydantic models for validation of this goal structure.

3. 3

   Design the Agent Plan

   Decompose the goal into an ordered list of sub-tasks. Generate multiple candidate plans (Plan A, Plan B) where feasible — evaluating each on: efficiency (which is faster?), cost (which is cheaper?), risk (what happens if it fails?), and alignment with constraints. Present plans to the human for selection if cost/risk is significant. Never hard-code a single plan — the agent should be capable of revising it.

4. 4

   Identify and Register Tools

   List every external API, service, or resource the agent may need to call (search engines, calendar APIs, job posting platforms, databases, email services, file systems). Register each as a LangChain tool. The LLM-brain will use reasoning to decide which tool to call at which step — do not hard-code tool calls to specific steps unless absolutely required.

5. 5

   Build the LangGraph Workflow

   Model the agent plan as a LangGraph StateGraph. Define: Nodes (each sub-task or agent action), Edges (sequential flow between nodes), Conditional Edges (branching logic — e.g., 'if applications < threshold → revise job description node'), Start and End nodes, State schema (use Pydantic for validation), Checkpointer (for persistent memory between runs). Choose workflow type: Sequential (ordered steps), Parallel (simultaneous independent sub-tasks), Conditional (branching on state), or Iterative (loops until a condition is met).

6. 6

   Implement Memory (Short-Term and Long-Term)

   Short-term memory: store current task state, tool responses, and intermediate outputs in the LangGraph state object (in-memory, session-scoped). Long-term memory: persist the full conversation history, goal metadata, and progress markers to a database (SQL, PostgreSQL, or vector store via SQLAlchemy). Use LangGraph's checkpointer to enable resume-from-last-state after interruption. Without long-term memory, the agent cannot answer 'what's the current status?' after a session gap.

7. 7

   Insert Human-in-the-Loop (HITL) Checkpoints

   Identify every step where autonomous action carries high risk or irreversibility (sending emails, spending money, rejecting candidates, writing to production databases). At each checkpoint: agent presents its proposed action and reasoning, pauses execution, waits for human approval (yes/modify/stop), resumes on approval. Implement override controls so humans can pause or halt the agent at any time.

8. 8

   Define Guardrails and Policies

   Hard-code non-negotiable constraints as guardrails the agent must never bypass regardless of reasoning — e.g., 'Never schedule on weekends', 'Never exceed budget X', 'Never discriminate by age or nationality'. Consider integrating Guardrails AI library for structured policy enforcement. These rules sit above the LLM's reasoning and cannot be overridden by a clever prompt.

9. 9

   Add RAG Integration (if required)

   If the agent needs to reference private documents or domain knowledge not in the LLM's training, connect a vector database (e.g., Chroma) as a knowledge base tool. The agent's LLM-brain will decide when to query the vector store versus answer from its own weights versus call a live external API — this decision is made via reasoning, not hard-coded routing.

10. 10

    Instrument with LangSmith Monitoring

    Connect LangSmith for agent tracing, step-by-step execution monitoring, and debugging. Log every tool call, LLM reasoning step, state transition, and HITL interaction. Use traces to identify where agents are making incorrect tool selections, looping unnecessarily, or failing silently.

11. 11

    Validate with Pydantic Throughout

    Use Pydantic models for: input validation (goal objects, user prompts), tool output validation (API responses before passing to LLM), state schema validation (LangGraph state objects), and LLM output parsing (structured responses from the agent). Pydantic prevents malformed data from propagating through multi-step workflows silently.

12. 12

    Deploy to Production

    Package the agent system using Docker. Expose via FastAPI backend. Set up environment variables for all API keys and database URLs. Configure GitHub Actions CI/CD pipeline. Deploy to AWS (for scalable production) or Render (for simpler managed deployment). Implement production folder structure, logging, and monitoring from day one — not as an afterthought.

// What are real-world examples of agentic AI systems in action?

// What mistakes should you avoid when building agentic AI?

• Giving the agent full autonomy without permission scopes — this causes irreversible actions like sending offer letters with wrong salaries or overspending on advertising budgets.
• Skipping HITL checkpoints to save time — without human-in-the-loop pauses at high-risk steps, agents will make consequential decisions (hiring, spending, emailing) without oversight.
• Using RAG when you need real-time data — RAG fails for continuously changing information (live news, weather, stock prices) because you cannot manually update the knowledge base fast enough; use a live tool/API instead.
• Confusing LangChain and LangGraph responsibilities — LangChain is for loading LLMs, prompt templates, and utility code; LangGraph is for building the agent workflow graph. Mixing them incorrectly produces unmaintainable code.
• Building without Pydantic validation — without structured input/output validation, malformed data silently propagates through multi-step workflows causing hard-to-debug failures downstream.
• Deploying without LangSmith monitoring — agents fail in non-obvious ways (wrong tool selection, reasoning loops, silent API errors); without tracing you cannot diagnose or fix production issues.
• Treating the agent's first plan as fixed — agents must be designed to adapt plans when conditions change (e.g., low applicant volume, tool API failure). Hard-coding a single linear plan removes the core value of agentic systems.
• Neglecting guardrails and ethical boundaries — without hard rules, agents may violate anti-discrimination laws, overspend, or generate inappropriate content. Guardrails must be defined explicitly and enforced above the LLM reasoning layer.
• Starting with agentic architecture when a simple LLM call or RAG pipeline would suffice — agentic systems are significantly more complex; always classify the task first and use the simplest architecture that solves the problem.
• Skipping async programming understanding — LangGraph uses asynchronous programming internally to run agents in parallel; not understanding async Python leads to blocking, sequential execution that defeats the purpose of parallel workflows.

// What are the key terms in agentic AI development?

Agentic AI

A type of AI that takes a task or goal from a user and works towards completing it on its own with minimal human guidance — planning, taking actions, adapting to changes, and seeking help only when necessary.

Knowledge Cutoff

The specific date up to which a large language model was trained on internet data. The model cannot answer questions about events or information after this date without external tools or RAG.

RAG (Retrieval-Augmented Generation)

A technique that connects an LLM to an external knowledge base (vector database) to answer questions beyond the model's knowledge cutoff, by retrieving relevant documents and passing them to the LLM for synthesis.

LangChain

The orchestration framework used to load LLMs, prompt templates, and write utility code. It is the foundational layer beneath LangGraph.

LangGraph

A graph-based agent workflow framework built by the LangChain team. It models agent workflows as StateGraphs with nodes (tasks), edges (flow), conditional edges (branching), and a checkpointer (persistent memory).

StateGraph

The core LangGraph construct — a directed graph where each node represents an agent action or sub-task, and edges define the control flow between them, including conditional and iterative paths.

HITL (Human-in-the-Loop)

Designed checkpoints within an agentic workflow where the agent pauses execution and requests human confirmation or input before continuing. Required at high-risk or irreversible action steps.

Orchestrator

The framework layer (LangGraph) that manages connections between the LLM brain, tools, memory, and HITL controls — enabling the construction of a complete agentic application.

Brain (LLM)

The large language model at the core of every agent that provides reasoning capacity — deciding when to call tools, how to decompose goals, when to request human input, and how to adapt plans.

Tool

Any external API, service, or resource the agent can call to perform actions beyond the LLM's internal knowledge — e.g., LinkedIn API, Google Calendar, web search, vector database, file system.

Permission Scopes

Explicit limits placed on what tools or actions an agent can execute independently, preventing fully unconstrained autonomy and reducing the risk of irreversible or harmful automated actions.

Guardrails

Hard rules and ethical boundaries defined by the developer that the agent must never violate regardless of its reasoning — e.g., never schedule on weekends, never exceed budget limits.

Checkpointer

A LangGraph component that persists the agent's state to a database between runs, enabling the agent to resume a workflow after interruption without losing progress.

Short-Term Memory

In-session state storage holding the current task metadata, tool responses, and intermediate outputs during a single agent workflow run.

Long-Term Memory

Persistent storage (database) of full conversation history, goal metadata, and progress markers that allows an agent to maintain context across multiple sessions.

Pydantic

A Python library used throughout agentic systems for data and model validation — validating inputs, LLM outputs, tool responses, and LangGraph state schemas to prevent malformed data from propagating.

LangSmith

A monitoring and tracing tool for agentic applications that logs every LLM call, tool invocation, state transition, and HITL interaction — used for debugging and production observability.

Sequential Workflow

A LangGraph workflow pattern where agent nodes execute one after another in a fixed order.

Parallel Workflow

A LangGraph workflow pattern where multiple agent nodes execute simultaneously, enabled by LangGraph's internal use of asynchronous Python.

Conditional Workflow

A LangGraph workflow pattern where the next node executed depends on the current state value — enabling branching logic within the agent graph.

Iterative Workflow

A LangGraph workflow pattern where a node or set of nodes loops until a specific condition is met (e.g., retry until sufficient applications are received).

Multi-Agent System

An architecture where multiple specialized agents collaborate — each as a node in a LangGraph — to complete a complex goal that is too large or multi-domain for a single agent.

// FREQUENTLY ASKED QUESTIONS