ai · 1 min read
ReAct, Plan-and-Execute, Reflexion: Choosing the Right Reasoning Loop
Three of the most influential agent reasoning loops side-by-side, with the workloads each one wins on and the Bhogar AI templates that ship them.
BABhogar AI TeamProduct & Engineering
A reasoning loop is the heartbeat of any agent. ReAct interleaves thought and action; Plan-and-Execute commits to a plan up front; Reflexion adds a critique step that lets the agent learn from its own mistakes within a single run.
Why it matters
Choosing the wrong loop is one of the most common architectural mistakes we see. ReAct is great for exploratory tasks but burns tokens. Plan-and-Execute is efficient for well-scoped jobs but brittle when the world changes mid-run. Reflexion improves quality but doubles latency.
How Bhogar AI approaches it
Bhogar AI ships all three as first-class templates, plus our Deep Agent which combines them adaptively - using cheap ReAct for discovery, switching to Plan-and-Execute once the task is understood, and invoking Reflexion only when the critic flags low confidence.
- Pre-built ReAct, Plan-and-Execute and Reflexion templates
- Adaptive Deep Agent loop for mixed workloads
- Per-loop budgets to cap latency and token spend
- Tracing UI to compare loops on the same input
- Eval harness to A/B reasoning loops with statistical significance
What you get
Customers picking the right loop per workload report 1.7-3× cost reduction at the same or higher quality, versus picking one loop for everything.