Production LLMs and AI Agents on OCI: Six Agent Orchestration Patterns

TL;DR: Not all agent workflows are equal. This pattern library maps six orchestration patterns to OCI services, with explicit decision criteria for each. Match your workflow requirements to the right pattern before you write a single line of code.

The Pattern Selection Problem

Most teams pick an orchestration pattern based on what they saw in a tutorial, not what their workflow actually requires.

This leads to:

• Over-engineering: Building complex multi-agent systems for simple linear workflows
• Under-engineering: Using linear chains when concurrent or collaborative patterns would dramatically improve quality
• Wrong abstraction: Fighting the pattern instead of leveraging it

The solution: Pattern-first design. Understand your workflow characteristics, then select the pattern that fits.

Pattern 1: Sequential Orchestration

Definition: Chains agents in a predefined linear order. Each agent processes the output from the previous agent, creating a pipeline of specialized transformations.

When to Use Sequential

OCI Implementation

Example: Contract Generation Pipeline

Pattern 2: Concurrent Orchestration

Definition: Runs multiple agents simultaneously on the same task. Each agent provides independent analysis from its unique perspective. Results are aggregated for final output.

When to Use Concurrent

OCI Implementation

Example: Investment Analysis

Pattern 3: Group Chat Orchestration

Definition: Multiple agents solve problems through shared conversation threads. A chat manager coordinates flow, determining which agents respond and when.

When to Use Group Chat

OCI Implementation

Maker-Checker Variant

A common group chat pattern where one agent creates and another validates:

Pattern 4: Handoff Orchestration

Definition: Enables dynamic delegation between specialized agents. Each agent assesses whether to handle the task directly or transfer to a more appropriate agent based on context.

When to Use Handoff

OCI Implementation

Example: Customer Service Escalation

Pattern 5: Orchestrator-Worker

Definition: A manager agent dynamically builds a task ledger with goals and subgoals, invoking specialized worker agents as needed. The plan evolves as context changes.

When to Use Orchestrator-Worker

OCI Implementation

Example: SRE Incident Response

Pattern 6: Human-in-the-Loop

Definition: Explicit breakpoints where human approval is required before the workflow continues. Critical for high-stakes decisions.

When to Use Human-in-the-Loop

OCI Implementation

Checkpoint Placement Strategy

Pattern Decision Matrix

Quick reference for pattern selection:

OCI Service Mapping Summary

What's Next

Part 3: The Operating Model — Evaluation pipelines, CI/CD for AI, incident response, cost management, and the 5-tier maturity roadmap.

Resources

• Microsoft Agent Framework Design Patterns
• Google Cloud Agent Design Patterns
• LangGraph Documentation
• OCI AI Agent Platform
• Oracle Open Agent Specification

This is Part 2 of a 3-part series on production LLM and agentic AI systems on OCI.

Part 1: Six-Plane Architecture | Part 3: Operating Model

Related Articles

• Part 1: Six-Plane Architecture — Foundation and design principles
• Part 3: Operating Model — Day-2 operations and governance
• Multi-Agent Orchestration Patterns — Coordination strategies that scale