Ten Ways To Wire AI Agent, And When To Use Each One
Ten Agentic AI Architecture Patterns, Read As Risk Decisions
Different Entry Point
Di normal way to show agent patterns na catalog. Di way wey dey more useful for leader na decision order. Start from di risk wey you ready to carry, den let dat one choose di pattern. Di same ten patterns go show, but di question go change from wetin dis fit do to wetin dis fit break.
From Lowest To Highest Autonomy
Start wit di single agent wit tools and di sequential pipeline. Dis ones na di low autonomy options. Dem dey predictable, you fit observe dem, and dem dey slow to surprise you. Dem na di correct first build for most enterprise tasks, because dem let you learn di workflow before you hand over control.
Next move go tool augmented retrieval and di reflection loop. Dis ones dey raise quality without raising di blast radius too much. Retrieval dey ground di system inside your own knowledge, and reflection dey force second look before output comot from di building. Di cost na tokens and latency, cost wey you fit measure and budget.
Step up go di planner and executor and di supervisor wit sub agents. For here autonomy dey increase because di system don dey decide how to break work apart and where to route am. Di reward na coverage across domains. Di new risk be say di coordinating logic itself fit dey wrong for ways wey hard to see, so monitoring dey shift from di output to di routing.
Reach di parallel fan out and reduce, event driven agents, and multi agent negotiation only when di tiers wey dey before don stable. Dis ones dey deliver breadth, responsiveness, and complex tradeoff handling. Dem dey also concentrate risk for di merge step, di event queue, and di negotiation logic respectively. Each one need real observability before e touch production decision.
Di Checkpoint Wey Make Di Rest Safe
Across every tier, di human in di loop checkpoint na di control wey dey turn autonomy into something wey enterprise fit defend. Di skill na placement. Put approval where di cost of wrong action dey high and irreversible, and remove am where actions dey cheap and reversible. Checkpoint for wrong place dey add delay without adding safety.
Comparison Framework
Set di reflection loop against di human in di loop checkpoint. Reflection na self correction by di system, fast and cheap compare to person, but e dey bounded by wetin di model don already know. Human approval na correction by person, slower and more expensive, but e fit catch di failure wey di model no fit see for imself. Use reflection to raise di floor for quality at scale, and use human approval to cap di downside for di decisions wey matter pass.
Three Lenses On Di Same Choice
For di executive, treat di tiers as governance ladder. You no dey buy agents, you dey decide how much independent action your processes fit absorb.
For di technical reader, instrument di boundary of each tier. Di dangerous transitions na where autonomy dey rise, so na there your traces, evals, and stop conditions suppose strong pass.
For di business, di payoff na adoption pace wey you fit defend. You dey move faster precisely because you fit show why each step dey safe.
If you rank your current AI initiatives by autonomy instead of by hype, which one dey sit one tier higher pass wetin im risk controls justify, and wetin you go change first?
Think mathematically. Engineer intelligently. Lead strategically. Build responsibly. Scale globally. Create real business value through artificial intelligence.
Originally published on LinkedIn by Keshavan Thiagaraja. Read the original on LinkedIn.