Core Architecture Patterns for Agentic Applications

Reference https://www.infoq.com/articles/prompts-to-production-playbook-for-agentic-development/

The article argues that agentic systems move beyond simple prompt/response flows and require repeatable architectural patterns to manage reasoning loops, tool use, coordination, and safety. These patterns act like design primitives for building production-grade AI systems.

1. ReAct Pattern (Reason → Act → Observe Loop)

Purpose: The foundational pattern for most agentic systems.

Instead of a single prompt/response interaction, the agent runs in a loop:

Reason → choose an action → execute tool → observe result → reason again

Key characteristics:

  • Iterative decision making
  • Tool invocation at each step
  • Feedback loop that refines future reasoning
  • Explicit termination condition

When to use it:

  • Multi-step problem solving
  • Research or debugging workflows
  • Tasks where intermediate results change the plan
  • Any workflow that cannot be solved in one prompt

This is described as the core building block from which many other patterns evolve.

2. Supervisor Pattern

Purpose: Coordinate multiple specialized agents.

A central supervisor agent:

  • Creates a high-level plan
  • Delegates tasks to worker agents
  • Tracks progress
  • Determines completion

Instead of one monolithic agent trying to do everything, you get:

  • Role specialization
  • Separation of concerns
  • Better scalability

When to use it:

  • Multi-domain workflows
  • Research pipelines
  • Complex orchestration tasks
  • Systems where expertise is distributed across agents

The supervisor acts like an orchestration layer, similar to a distributed systems controller.

3. Hierarchical Agent Pattern

Purpose: Scale beyond a single supervisor bottleneck.

As systems grow, one supervisor becomes overloaded. The hierarchical pattern introduces layers:

Global supervisor → regional supervisors → worker agents

This structure:

  • Reduces coordination complexity
  • Enables parallelism
  • Supports organizational scaling

When to use it:

  • Enterprise-scale agent ecosystems
  • Large operational workflows
  • Systems with geographic or functional partitioning
  • Environments with many independent subdomains

It mirrors human organizational structures and distributed system hierarchies.

4. Human-in-the-Loop Pattern

Purpose: Add safety, judgment, and governance.

Some decisions should not be fully automated. This pattern inserts human checkpoints:

  • Approval gates
  • Review steps
  • Override controls
  • Ethical/compliance validation

When to use it:

  • High-risk decisions
  • Legal/financial workflows
  • Regulated industries
  • Safety-critical operations

The article emphasizes that production agent systems must be designed for accountability, not just autonomy.

Supporting Patterns (Mentioned Briefly)

The article also references additional orchestration styles used in specialized cases:

  • Sequential orchestration (pipeline execution)
  • Concurrent orchestration (parallel tasks)
  • Handoff pattern (dynamic delegation)
  • Event-driven agents
  • Scheduler–agent–supervisor workflows
  • Blackboard coordination
  • Market-based agent negotiation

These extend the core patterns depending on system needs.

Key Architectural Insight

The central takeaway:

Agentic systems are not prompt engineering problems — they are architecture problems.

Successful production systems:

  • Treat agents as distributed components
  • Use structured coordination patterns
  • Explicitly design for iteration, delegation, and control
  • Balance autonomy with oversight

The article frames these patterns as the foundation for moving from demos to reliable, production-grade agent platforms.