Agent architectures

Here, architecture means the decision structure around the model. When does it call a tool? Is the plan made once or updated after each observation? Is failure written into memory? Are multiple solution paths explored?

The same model can behave like an operator in ReAct, a coordinator in Plan-and-Execute, a student in Reflexion, a search process in Tree of Thoughts, or a reasoning graph in Graph of Thoughts.

ReAct puts reasoning and acting in the same loop. The model reasons, calls a tool when needed, receives an observation, and updates the next step.

It is useful when the task lacks information, but every tool call adds latency and a new failure surface.

Plan-and-Execute decomposes the task before execution. Plan-and-Solve does this at prompt level; HuggingGPT turns it into model/tool orchestration.

The strength is reducing missing steps. The risk is that a bad global plan can make the whole system execute the wrong thing cleanly.

Reflexion lets an agent learn from failure without changing model weights. Feedback becomes a natural-language lesson stored in episodic memory.

This works best when the environment can provide evaluable signals such as tests; it is weaker when broad exploration is the bottleneck.

Tree of Thoughts treats reasoning as search over intermediate thoughts instead of one left-to-right completion.

It helps when lookahead and backtracking matter, but it trades tokens and latency for reliability.

Graph of Thoughts relaxes the tree assumption. Different reasoning paths can merge, improve each other, or contribute to a shared intermediate result.

This fits tasks that naturally decompose and recombine, but it requires more controller, scorer, parser, and validation machinery.

Do not ask which architecture is best. Ask where the agent breaks: missing information, missing steps, repeated mistakes, premature commitment, or inability to merge parts.

Reliability is not a model property alone; it is a fit between task and control structure.

Agent architectures mature when control flow, state, feedback, search, and validation are combined in a way that matches the task.

So before choosing an architecture, ask what kind of uncertainty the system faces.