Agents Everywhere: Part 2 - Three Deployment Architectures Shaping Modern AI

Three Architectures, Three Different Realities

There are 3 ways most organisations are deploying AI agents today. Only one of them scales reliably. And the gap between them isn't just technical — it's architectural.

Most conversations about AI agents skip past this reality entirely. They focus on what agents can do without asking how they're actually structured in production. In practice, teams consistently fall into one of three deployment patterns — each with different trade-offs, different failure modes, and different ceilings.

The challenge isn't building agents anymore. It's managing them — at the right architecture for your current stage.

Classification of AI agents from reactive to fully autonomous

Architecture 1 — Single-Agent Systems

This is where almost everyone starts. One language model, a curated set of tools, and some decision logic. Simple, predictable, and — within its limits — genuinely effective.

Where it works well: Internal assistants that answer questions from a knowledge base. Content generation pipelines. Customer support triage and routing. Lightweight research automation.

Where it breaks: Tasks that require parallel work, specialised domain expertise across multiple areas, or complex multi-step decision trees. Single-agent systems hit a capability ceiling quickly once scope expands.

Lowcoordination overhead — the main advantage over more complex setups

Architecture 2 — Multi-Agent Systems

The moment a task exceeds what a single agent can handle cleanly, teams naturally move toward decomposition: break the work into specialised roles and assign different agents to each. A planner. An executor. A reviewer. A summariser.

This is where things get genuinely interesting — and genuinely fragile.

How multi-agent setups enable specialisation and parallel execution

Each agent is optimised for its role. A research agent with deep web access. A writing agent with creative prompt tuning. A fact-checking agent with structured verification logic.

Independent tasks can run simultaneously. A pipeline that previously took minutes sequentially now takes seconds when agents work in parallel.

With multiple agents, you need to manage data handoff, sequencing, conflict resolution, and failure recovery. What starts as "agents collaborating" can quickly become "agents interfering with each other."

Architecture 3 — Orchestrated Systems

This is the architecture that actually scales. It adds a coordination layer — something above individual agents that manages task assignment, monitors execution, handles failures, and enforces policies.

The orchestrator isn't necessarily an LLM. It might be a deterministic workflow engine, a rule-based router, or a lightweight control script. What matters is that it exists — and that someone designed it deliberately.

An orchestration layer coordinating multiple agents and workflows

Architecture Comparison

Criterion

Single-Agent

Multi-Agent

Orchestrated

Setup complexityLowMediumHigh

ScalabilityLimitedModerateHigh

Reliability in productionHighVariableHigh (when designed well)

ObservabilityEasyDifficultDesigned in

Failure mode severityLocalisedCascadingManaged

Cost per taskLowestHighestOptimised

Which Architecture Should You Choose?

More agents doesn't equal a better system. The goal is the right architecture for the problem — not the most impressive one on paper.

Orchestration isn't a feature you add later. It's a design decision you make from the beginning.

What Breaks in Practice

Now that we understand the architectures, Part 3 digs into where agentic systems actually fail — and what those failures tell us about the future of reliable AI.

Read Part 3: Why Systems Break →