{"post":{"slug":"react-agents-explained-2026","title":"ReAct Agents: Interleaving Reasoning and Tool Calls","excerpt":"Think, act, observe, repeat. The pattern behind most agent frameworks is simpler than the frameworks suggest, and understanding it explains most of how agents fail.","category":"ai","date":"January 13, 2026","author":"ByteLedger Team","content":"<span class=\"dropcap\">S</span>trip away the frameworks and most agents run the same loop. The model produces a short piece of reasoning about what it should do, emits a tool call, receives the result, and reasons about that result before deciding the next step.\n\nThat interleaving — reasoning and acting alternating rather than one long plan followed by execution — is the pattern. It works because the model gets to revise its approach based on what it actually observed, rather than committing to a plan built on assumptions.\n\n## What changed in 2026\n\n- **The pattern became implicit rather than prompted.** Models trained for tool use exhibit it natively, so the explicit thought-action-observation scaffolding in prompts became largely unnecessary.\n- **Context management became the limiting factor.** As loops got longer, accumulated observations rather than reasoning quality became the constraint.\n- **Convergence controls became standard.** Step caps, budgets, and progress detection moved from good practice to requirements.\n- **Structured tool calling replaced text parsing.** Native tool-call formats removed a whole class of parsing failure the original pattern suffered from.\n\n## The loop, and where the cost lives\n\nEach iteration adds three things to the conversation: the model's reasoning, the tool call, and the tool's result. All three stay in context for every subsequent turn.\n\nThe observations are the expensive part. A tool returning a large file, a long search result, or verbose API output puts all of it into the window, permanently, and the model re-reads it on every remaining turn.\n\n| Iteration | Context contains |\n| --- | --- |\n| 1 | Prompt, reasoning, call, result |\n| 5 | Everything from iterations 1–4, plus this one |\n| 20 | Twenty rounds of accumulated tool output |\n\nWhich is why a twenty-step agent run is far more expensive than twenty single calls, and why [prefix caching](/blog/prefix-caching-explained-2026) matters so much — each turn's prefix is the previous turn, so almost everything is cacheable.\n\nIt is also why [context compaction](/blog/context-compaction-explained-2026) and clearing stale tool results become necessary rather than optional past a certain loop length.\n\n## How it fails\n\n**Non-convergence.** The model repeats an action, gets the same result, and tries again. Nothing errors; it simply never decides it is finished. This is the single most common agent failure and the reason step caps and [token budgets](/blog/agent-token-budgets-2026) are mandatory rather than advisable.\n\n**Reasoning that is not verification.** The model states \"I have now confirmed the value is 42\" in text that reads like a check and was produced by the same process that produced the value. Reasoning text is a plan, not evidence — treating it as validation is how confidently wrong conclusions survive.\n\n**Context poisoning.** A wrong observation early — a stale document, a tool returning a cached value — propagates through every subsequent step, and the agent grows more confident as it accumulates reasoning built on it. See [context poisoning](/blog/context-poisoning-explained-2026).\n\n**Tool errors handled badly.** A failed call returning an unhelpful message leaves the model guessing. Error text is part of your prompt surface and deserves the same care as tool descriptions — see [agent tool error handling](/blog/agent-tool-error-handling-2026).\n\n## When a workflow is better\n\nThe pattern's value is handling tasks you could not enumerate in advance. If you *can* enumerate them, you should.\n\nA task with a known fixed sequence — extract, validate, transform, store — is a workflow. Writing it as deterministic control flow with model calls at the steps that need judgement is cheaper, faster, more predictable, and far easier to debug than letting a model rediscover the sequence each time.\n\nThe honest test: could you write the steps down? If yes, write them down. Reserve the agent loop for genuine exploration.\n\n## Common mistakes\n\n- **No step cap.** Non-convergence becomes an unbounded bill.\n- **Treating reasoning as verification.** Same process, no independent check.\n- **Letting observations accumulate unmanaged.** Context fills with stale tool output.\n- **Using it for a known sequence.** A workflow is better in every respect.\n- **Unhelpful tool error messages.** The model cannot recover from what it cannot understand.\n- **No tracing.** A twenty-step failure is undiagnosable without per-step spans.\n\n## FAQ\n\n**Do I need to prompt the pattern explicitly?**\nRarely on current models — tool-use training makes it native. Explicit thought-action-observation scaffolding is mostly legacy and consumes context.\n\n**How many steps is reasonable?**\nTask-dependent, and the useful discipline is measuring your own distribution and capping above the legitimate tail. Most tasks converge in far fewer steps than the cap you would set.\n\n**How does this differ from tree search?**\nReAct commits to one path and revises as it goes. Tree search explores alternatives and backtracks. Search is more thorough and much more expensive — see [tree search agents](/blog/tree-search-agents-2026).\n\n**What about reflection?**\nAdding an explicit critique step is a common extension that helps on some tasks and adds cost on all of them — see [agent reflection loops](/blog/agent-reflection-loops-2026).\n\n## Where to go next\n\nFor bounding the loop, read [agent token budgets](/blog/agent-token-budgets-2026) and [agent timeouts](/blog/agent-timeouts-explained-2026). For the tracing that makes failures diagnosable, [AI agent observability](/blog/ai-agent-observability-2026).","hue":220,"metaTitle":"ReAct Agents: Interleaving Reasoning and Actions","metaDescription":"How the ReAct pattern alternates reasoning and tool calls, why observations accumulate in context, and the convergence failures it produces.","keywords":["react agents","reasoning and acting","agent loop pattern","tool use loop","agent convergence","observation accumulation","agent architecture","llm agents 2026"],"tldr":["**Reasoning between actions is the whole idea** — the model states what it is doing before doing it, and interprets the result after.","**Each observation enters the context permanently** — which is why long loops fill the window with tool output.","**Loops that fail to converge are the characteristic failure** — the model repeats an action expecting a different result.","**The reasoning text is not verification** — it is generated by the same process that chose the action.","**Skip:** the pattern for a task with a known fixed sequence. A workflow is more predictable and cheaper."],"readTime":"4 min read","wordCount":840},"related":[{"slug":"agentic-workflows-explained-2026","title":"Agentic workflows explained for 2026","excerpt":"An agentic workflow lets a model plan, call tools, and check its own work across multiple steps instead of answering in one shot. Here is how the common patterns actually work.","category":"ai","date":"July 1, 2026","author":"ByteLedger Team","readTime":"4 min read","hue":310},{"slug":"ai-agents-tutorial-2026","title":"AI agents tutorial in 2026: build your first agent in 30 minutes","excerpt":"Build a working AI agent in 2026 with the minimum viable stack — one model call, two tools, and a loop. 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