Every Agent Reasoning & Planning comparison and buyer's guide for building AI agents — 19 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
The three multi-agent shapes aren't ranked best-to-worst — they're a single axis. Pick by one question: how much context can you afford to lose between agents?
Making several agents argue toward consensus does raise accuracy a few points — but a single model sampled the same number of times, at the same cost, usually matches it, and debate has a failure mode solo sampling doesn't.
The field spent a year making the orchestrator smarter. Microsoft's Conductor argues the routing layer should be dumb — and spend zero tokens deciding what runs next.
Telling an agent to review its own reasoning usually makes it worse, not better — and the reason it fails is the same reason Reflexion succeeds. Both come down to one asymmetry: verifying is only easier than generating when the verifier knows something the generator doesn't.
The point of thinking between tool calls isn't a smarter first plan — a model can plan up front without it. The point is that the model can notice a tool returned something wrong and re-plan on the spot, instead of barreling ahead.
Mixture-of-Agents wins by quality, not by variety — and a careful 2025 replication found that aggregating repeated samples from your single best model beats mixing different ones in most cases. Here's when an ensemble actually pays, and when it just adds latency.
Four ways to make an agent fix its own mistakes. Three of them quietly outsource the judgment to the world — and the one that doesn't is the one the research keeps catching in the act.
A max-step counter is the reflex, and it's necessary — but it caps the damage without fixing the cause. Agents loop because the thing they see never changes, and that's a fixable problem.
A deep agent is not a new model or a framework breakthrough — it's four cheap, known ingredients that let a plain tool-calling loop survive a long task instead of drifting.
Both spend N times the inference to make a model smarter. The difference is how they choose the winner — and that choice decides which tasks each one can help.
Every lab gives you a dial for how hard a model reasons before it answers — through three incompatible interfaces. The surprise is that turning it up isn't always better.
The topology you pick for your agents is really one decision in disguise — who holds the state and the control — and that single choice sets your token bill, your latency, and whether you can ever debug the thing.
Pausing an agent for a human approval is the same engineering problem as surviving a crash — both require serializing the run and resuming it later. Here's why, and what each framework gives you.
They were taught as a quality ladder. They're not — and on reasoning models the ladder is upside down. A field guide to which prompting style actually helps which model.
Test-time compute makes the model think harder while the user waits. Sleep-time compute moves that thinking off the critical path — but only pays off when the context is known early and reused across queries.
The architecture decision underneath every agent framework is one most teams skip — and the math of compounding errors says the boring choice is usually right.
The listicle treats these as three flavors of the same choice. They aren't — two are ends of one axis, and the third sits on a different axis entirely. Pick by your environment, not your vibe.
A reasoning model is not a better LLM. It is a compute-allocation choice — and the trade only pays off on a specific shape of problem.
Two of the most-cited essays on agent design say opposite things. They are both right — the disagreement is really about whether your task reads or writes.
Not buyer's guides — the news, teardowns, and explainers behind this topic.