Agentic AI Design Patterns — Part 4: Control, Multi-Agent & Production

Introduction

This is the final post in the agentic-ai-overview series:

Parts 1–3 built single-agent and workflow skills. Part 4 adds control (safety, memory, humans), multi-agent scale, and the production docs for shipping beyond python main.py.

Pattern Map (07–15)

07 — Orchestrator–Workers

What it is: A central orchestrator decomposes work and delegates to specialist workers — each with its own prompt, tools, and optionally a compiled subgraph.

Best scenario: demand-forecast — MLDLC specialists (data prep, training, deployment).

python patterns/07-orchestrator-workers/example/main.py --scenario demand-forecast --no-mlflow --stream-events

Workers are LangGraph subgraphs — inspect shared/examples/graphs/ for reusable worker builders.

08 — Evaluator–Optimizer (Reflection)

What it is: Generate → critique → revise loop. A critic (same or separate LLM call) scores output and feeds back improvement instructions.

When to use: Customer-facing replies, report quality, any task where first-draft LLM output is not good enough.

This is the same Generator–Critic pattern used in the social multi-agent feed project (post_composer ↔ validator).

python patterns/08-evaluator-optimizer/example/main.py --scenario ecommerce

09 — Human-in-the-Loop (HITL)

What it is: Pause before side effects — ticket creation, refunds, model registration — using LangGraph interrupts and checkpoint resume.

Features in this repo: SQLite checkpoints (data/checkpoints/), --auto-approve for demos, --time-travel to rewind state.

python patterns/09-human-in-the-loop/example/main.py --scenario helpdesk --auto-approve --time-travel

10 — Memory & Context Management

What it is: Short-term (thread messages, checkpoints) and long-term (persisted facts across sessions).

python patterns/10-memory/example/main.py --scenario helpdesk

Pair with HITL: checkpoints enable both human approval and multi-turn follow-ups on the same thread_id.

11 — RAG (Retrieval-Augmented Generation)

What it is: Retrieve relevant chunks from a corpus before generation — internal FAQ, playbooks, policy docs.

Best scenario: helpdesk — markdown FAQ corpora with retrieve_from_markdown helpers in the scenario module.

python patterns/11-rag/example/main.py --scenario helpdesk

Production scaling (indexing, refresh, chunking) is covered in docs/architecture/production/rag-at-scale.md.

12 — Guardrails & Safety

What it is: Input/output/policy checks before and after the agent loop — PII redaction, blocked topics, tool argument validation.

python patterns/12-guardrails/example/main.py --scenario ecommerce

Add guardrails before destructive tools in any demo you show to stakeholders.

13 — Handoff / Delegation

What it is: Transfer control and context from one agent persona to another mid-conversation — Tier-1 support → Tier-2 specialist.

Best scenario: helpdesk with handoff_escalation_prompt in the scenario registry.

python patterns/13-handoff/example/main.py --scenario helpdesk

Differs from 07 Orchestrator–Workers: handoff is sequential control transfer, not parallel delegation from a planner.

14 — Map–Reduce

What it is: Map many chunks in parallel (summarize, classify, extract), reduce into one aggregate result.

python patterns/14-map-reduce/example/main.py --scenario helpdesk

15 — Event-Driven Agents

What it is: Agents triggered by events (inbound email, schedule, model drift) rather than synchronous chat.

Best scenario: demand-forecast — retrain trigger when drift is detected.

python patterns/15-event-driven/example/main.py --scenario demand-forecast --stream-events

Cross-Cutting Patterns (Attach to Any Architecture)

These patterns work in both single-agent and multi-agent setups:

From Examples to Production

The repo's docs/architecture/production/ folder bridges pattern examples → deployable systems:

Production topology (reference)

Minimal Production Stack (Recommended First Ship)

Do not start with 07 Orchestrator–Workers in production unless single-agent genuinely failed you.

Series Recap

Over four posts we covered agentic-ai-overview:

1. The catalog — 15 patterns, learning path, single vs multi-agent
2. Shared runtime — three scenarios, CLI, MCP, LangGraph conventions
3. Foundation patterns — ReAct through Parallelization with runnable commands
4. Advanced patterns — orchestration, safety, memory, events, and production guides

The repo is GPL-3.0 licensed, Python 100%, with Bangla documentation at README.bn.md.

Pair this series with: the social multi-agent feed project for a full-stack example of Generator–Critic, tool use, and multi-persona agents in production-shaped code.

Clone it, run pattern 01 today, and add patterns only when your use case demands them — that is the point of the catalog.