Ngezinye I-ReAct Design Pattern: Indlela I-AI Modern Thinks and Acts

Usuku olunye, inguqulo yesinye se-AI ibonise i-time line. Lesi xesha, i-ReAct (i-nope, akuyona i-JavaScript owaziwa futhi uyakuthanda). Thina ushiye mayelana ne-ReActUkuphendula + Ukuphendulaisampula esizayo esizayo ehlabathini we-AI agents.

Okokuqala yasungulwa ngo-2022 (eyenziwe ngempumelelo eminyakeni ye-AI), isampula ye-ReAct kuyinto ngokushesha emhlabeni wonke - futhi ngezizathu olungcono ... Funda ngokushesha lapho sincoma ukuthi kuyinto, indlela yokusebenza, futhi indlela yokusebenza ku-agent workflow yakho.

I-AI wave? Nah. It's time to Re-Act!

Yini i-ReAct Design Pattern?

Ngaba uyazi, "Ugh... enye inguqulo React ngo-2025? Hhayi siye sinxazulululwe kuleli ... imizuzu? Kuyinto React ... kodwa ku-AI manje?“Ou mhlawumbe”Okuningi, Ngingathanda Izakhiwo ze-React!”

Hlola!We're talking about a different kind of ReAct here!

Umhlaba we-AIUkuphendula- okuyinto etholakalayo kusuka ku-"I-Reasoning" + "I-Acting" - iyisisombululo sokucubungula lapho i-LLM ihlanganisa ukucubungula nokuphendula izinhlelo zokusebenza kakhulu noma ukukhiqiza imiphumela enhle futhi enhle.

👇 Let's break it up nge analogy okusha! 👇

Umzekele ukuthi utshale umbhobho we-AI . Uma ushiye nje "ukwenza i-sandwich", uhlelo lwe-AI ephakeme ingatholela i-LLM izicelo kanye nokuthumela isitifiketi se-static.

NgiyaReAct-powered agent? Umdlalo olukhulu! Okokuqala, itreasons: "Ukuvela - yintoni i-sandwich? Ngingathanda izakhiwo? Yini i-bread?" Ngemuva kwalokhoacts: ivula i-freezer, ukuthatha ukuthi kuyimfuneko, izikhwama, izikhwama, futhi voilà-BLT ephelele!

Ngokuvamile, ReAct akufanele kuphela. Itizakhiwo, izakhiwoWazeUkusebenzaUkusuka. Ukusuka. Ukusuka.

Ukulungiswa okokuqala le pattern ku-2022 iphepha "ReAct: Synergizing Ukuphendula nokuphendula ku-Language Models“Ukuhlukanisa ku-2025 njengomthombo we-Agentic AI kanye ne-Agentic RAG-based agents.

Okunye, kanjani lokhu kungenzeka, futhi indlela le design pattern ngokwenene ukusebenza? Thina ufunde!

I-ReAct Origins: Indlela I-2022 I-Paper Yenza I-AI Workflow Revolution

Ngemuva kwe-2022, iReAct: Synergizing Ukuphendula nokuphendula ku-Language ModelsImininingwane ezisekelwe kuleli khasi:

“I-LLM’s’ abilities for reasoning (isib. Chain-of-thought-prompting) kanye ne-acting (isib. Ukwakhiwa kwe-action plan) zithunyelwe ikakhulukazi njengezinto ezahlukile. [Here, we] ukubuyekeza ukusetshenziswa kwe-LLM’s ukukhiqiza izimpendulo ezimbini kanye nemisebenzi esifundeni ngokuvamile ngokuvamile...”

“I-LLM’s’ abilities for reasoning (isib. Chain-of-thought-prompting) kanye ne-acting (isib. Ukwakhiwa kwe-action plan) zithunyelwe ikakhulukazi njengezinto ezahlukile. [Here, we] ukubuyekeza ukusetshenziswa kwe-LLM’s ukukhiqiza izimpendulo ezimbini kanye nemisebenzi esifundeni ngokuvamile ngokuvamile...”

Ngokuvamile: + = 💥.

Okwangoku, i-LLM yaba ikakhulukazi ama-assistants e-brainy—i-generating text, ukuphendula imibuzo, ukudala ikhodi. Kodwa keUkuguqulwaNgaphansi kwe-2022 (Yep, lapho i-ChatGPT yasungulwa ngoNovemba 30), ama-developer basungula ukuqhuma i-LLM ku-workflows ye-software enhle. Izinto zilungele.

Fast forward to today: Ngena ngemvume kuUsuku I-AI Agents– izinhlelo zokusebenza okuzenzakalelayo, zihlanganisa, ukucubungula, nokwenza izinto.

Kule NewAI “Agent” era, isampula ye-ReAct - okwenziwe nje isampula se-academic - iyona omunyeIzakhiwo ezivamileUkwakhiwa kwama-I.I.I.Agents eyenziwe ngempumelelo. Ngaphezu kwalokho, i-IBM inikeza i-ReAct njenge-building block ye-agentic RAG workflows:

Okay, ngakho-ke ReAct iyatholakala kumadlulayo ... kodwa kusetshenziselwa elandelayo.

Sishayele DeLorean (88 MPH, baby! ⚡) - Sishayele ekhukhwini ukuze ubone kanjani le pattern kusebenza emzimbeni, futhi indlela yokusebenza.

Ukusebenza kwe-React ku-Agentic AI Workflows

Thola React njengobaMacGyver of AI

Ngaphandle kokufaka nje impendulo efana ne-LLM yakho, izinhlelo ze-ReActTholaNgena ngemvaukunakekelwaNgemuva kwalokhoUkulungele. It is not magic ✨ – it is when chain-of-thought reasoning met real-world action.

Ngokuvamile, i-ReAct agent isekelwe aThink 🤔 → Act 🛠️ → Observe 🔍 → Repeat 🔁Ukubuyekezwa:

1. I-Reasoning (Think 🤔): Ukuqala nge-snap like "I-Plan a weekend trip to NYC." I-Agent ikhiqiza imibuzo: "Ngingathanda izindiza, i-hotel, kanye nenkinga le-attractions."
2. Ukukhetha isinyathelo (Act ️): Ngokusekelwe isinyathelo se-agent, inikeza isixhobo (isibonelo, ngokusebenzisa ukuhlanganiswa kwe-MCP) - bheka, i-API yokufunda izindiza - futhi isebenza.
3. Ukucaciswa (Ukucaciswa ): Izixhobo ivumela idatha (isib. Izinhlelo zokuhamba). Lokhu kusetshenziselwa kwama-agent, okuyinto zihlanganisa isinyathelo esilandelayo sokucaciswa.

Loop (Repeat 🔁)I-Agent isebenzisa imibuzo ezintsha ukhethe isixhobo esinye (isib. Ukukhangela i-hotel), inikeza idatha ezingaphezu, ukuhlaziywa kwegama yayo - konke ngaphakathi kwe-top-level loop.

Ungathanda ukuthi ukuchithwa kwe- "ukuba akuyona" ikhoyili. Kule ngalinye iteration, umphakeli:

• Yenza isinyathelo esisha sokucindezela.
• Khetha imishini elihle yokusebenza.
• Yenza umsebenzi.
• Ukuhlobisa imiphumela.
• Ukubuyekeza ukuba izinga lokuphumula.

Ukulinganiswa okuqhubekayo kuze kube luhlobo lokuphendula noma indawo yokufinyelela.

Indlela yokwenza ReAct

Ngakho-ke, ufuna ukufaka i-ReAct ekusebenzeni ne-agents ye-real-world? Ngiya ku-common setup!

Ukukhishwa kwe-show nge-AOrchestrator Agent(Ukuza)think CrewAI noma isakhiwo esifanayoI-agent ye-top-level, eyenziwe nge-LLM yakho ye-choice, inikeza isicelo sokuqala ku-DedicatedI-Agent ye-Reasoning.

WazeReasoning AgentNgaphandle kokuphuma,UkuhlukanisaI-Prompt ye-Original ku-liste esifanele ye-steps noma i-sub-tasks eyenziwe ngempumelelo. It is the brain, meticulously planning the strategy.

Ngemuva kwalokho, lezi izicelo zithunyelwe ku-aActing AgentKuyinto lapho ingxubevange isitimela isitimela! Le agent kuyinto tool-wielder yakho, ehlanganisiwe ngqo nge-MCP server (ukwazi ukufinyelela idatha ezingaphandle noma izixhobo ezifana web scrapers noma databases) noma ukuxhumananezinye ama-agents ezijwayelekile ngokusebenzisa i-A2A protocols. It's tasked with actually Ukusebenzaizindlela ezidingekayo.

Iziphumo zezi zokusebenza zangaphandle. Zihlanganisa ku-Observing AgentI-agent yenza ukubuyekeza imiphumela, ukunqoba ukuthi umsebenzi iyatholakala futhi ephelele, noma uma izinyathelo ezininzi zihlanganisa. Uma izinyathelo ezilandelayo zihlanganisa, isikhwama uqala ngokushesha, ukunikela ama-agent ngokushesha inqubo.

OkuqhubekayoReasoning -> Acting -> Observingumzila wahlala kuze kubeumphathi we-AgentUkubonisa umphumela "yakhelwe", ukuxhumana okuhlolwa okuqhubekayo kuze kuUmphathi we-OrchestratorNgiyaxolisa ku-Inquirer.

Njengoba ungakwazi ukubona, indlela elula ukufaka i-ReAct kuyinto nge-multi-agent setup! Nokho,UkulungeleThola nje nge-single, elula, mini agent, futhi. Just check out the example in the video ngezansi:

ReAct vs “regular” AI Workflows

Core Process

I-Direct Generation; I-Single Inference Pass

I-Iterative "I-Rasoning + Acting" isilinganiso; ukuyila kanye nokwenza isilinganiso

External interaction

Ungafaki ku-no external tool usebenzisa

Ukusebenza okuzenzakalelayo Instruments

Adaptability

Okungenani adjustable; isekelwe data ukuqeqeshwa.

Ukuhlobisa kakhulu; Izinzuzo zokusekelwe ku-feedback e-real-time.

Problem solving

I-Best for Simple, i-Single-Turn Task.

I-Excels ku-complex, i-multi-step problems requiring external info and dynamic solutions

Feedback Loop

Ngokuvamile akukho ukubuyekeza ngokuvumelana self-correction

I-explicit-real-time feedback loop ukucubungula ukucubungula kanye nokuguqulwa kwezinto

Transparency

Ngokuvamile ibhokisi black; okungenani ukucindezeleka logic.

Ukuhlobisa okuphezulu; Chain-of-Thought kanye nezimo ezisebenzayo zibonisa isisombululo kanye nokukhipha ngalinye iminyango

Use case fit

Simple Q & A, ukwakhiwa kwekhwalithi

Izinqubo ezinzima: Ukuhlolwa, Ukuhlolwa, Imisebenzi ye-multi-tool

Implementation

Simple; kufuneka AI chat ukuhlanganiswa

I-complex; inikeza i-luke logic, ukuhlanganiswa kwezixhobo, futhi ingatholakala isakhiwo se-multi-agent

Izinzuzo nezinzuzo

👍 Super accurate and adaptableThinks, Actes, Learns, and Course-corrects on the fly.👍 Handles gnarly problemsI-Excels ku-complex, i-multi-step tasks eyenza i-info ye-external👍 External tool power: I-Integrates nge-instruments ezisebenzayo namafutha ze-external.👍 Transparent and debuggable: Hlola wonke umqondo kanye nemisebenzi, ukwenza ukucubungula umlilo.

👎 Increased complexityImininingwane engaphezu kwama-Moving Parts kuyinto engaphezu kwe-Design ne-Management.👎 Higher latency and callsI-Iterative loops, izivakashi ze-external, kanye ne-orchestration overhead kungenzeka ukuthi izindleko zokusebenza zangaphambili futhi izivakashi zihlukile kakhulu (e-cost to pay for more power and accuracy).

Yini kufuneka ukuba Master ReAct

Thina siphinde – ngaphandle kwezindlela ezifanele, i-agent ye-ReAct ayikho kakhulu enhle kunoma iyiphi enye inqubo ye-AI yokusebenza. Izixhobo zibonisa ukubuyekeza ekusebenzeni. Ngaphandle kwabo, ama-agents akuyona kuphela ... ukuhlala kakhulu.

Ku-Bright Data, sinamathela ukuxhumanisa ama-agents e-AI ku-tools ezinhle. Ngakho-ke, sinikeza isakhiwo ephelele ukucubungula lokhu. Akungekho indlela yokucubungula ama-agents akho, sinamathela:

• I-Data Packs: I-curated, i-real-time, i-AI-ready datasets enhle yokusebenza kwe-RAG.
• I-MCP servers: I-AI-ready servers ifakwe nge-tools ye-data parsing, i-browser control, i-format conversion, nokunye. ️
• I-SERP APIs: Hlola i-APIs i-LLM yakho angakwazi ukufinyelela iziphumo ze-web ezingenalutho, ezingenalutho - eyakhelwe ama-RAG pipelines.
• Izibuyekezo ze-Agent: Izibuyekezo eziholile ze-AI ezivela ku-web, ukuhlangabezana nezinsizakalo ze-IP, ukuhlangabezana ne-CAPTCHAs, futhi zihlole. ️

... Futhi le toolstack iyatholakala ngokushesha.

➡️Take a look at what Bright Data’s AI & BI infrastructure can unlock for your next-gen agents.

[Extra] I-ReAct Cheat Sheet

Ngaphambi kokufaka, ukuthatha imizuzu yokuhlanza emoyeni. Kubalulekile kakhulu (ne-confusion) mayelana ne-terms "ReAct" - ikakhulukazi njengoba amabhizinisi amaningi abasebenzisa ku-contexts ahlukene.

Ngakho-ke, apha i-non-fluff glossary yokusiza ukugcina yonke okuhlobene:

• "ReAct design pattern": Isakhiwo se-AI esihlanganisa ukubuyekeza nokuphendula. Umthengisi wabheka okokuqala (njenge-ketch-of-thought ukubuyekeza), bese isebenza (njenge-web search), futhi ekugcineni inikeza impendulo enhle.
• "I-ReAct prompting": Isinyathelo se-prompt-engineering enikeza i-LLM ukubonisa inqubo yayo yokuxhumana isinyathelo esisodwa kanye nokuthatha imiphumela esisodwa. It yenzelwe ukwenza imibuzo enhle, enhle, futhi engaphansi kwe-hallucination. Funda kabanzi mayelana ne-ReAct prompting.
• “ReAct agentic pattern”: Just another name for saying “ReAct design pattern.”
• "I-ReAct Agent": Yonke i-AI agent elandelayo isilinganiso se-ReAct. I-ReAct inikeza imiyalezo mayelana ne-task, isebenza imiyalezo esekelwe kulesi isilinganiso (njenge-Calling a Tool), futhi ivumela impendulo.
• I-ReAct Agent Framework: I-architecture (noma i-library) ebonakalayo ukwakha ama-Agents e-ReAct. It inikeza ukuvelisa yonke i-logic ye-reason-act-answer emakhasini yakho ye-AI.

Final Thoughts

Ngaba ufunde ukuthi i-ReAct inikeza kanjani ku-AI – ikakhulukazi lapho ithi ama-agents e-AI. Uyazi ukuthi lokhu isampula sokucubungula etholakalayo, ukuthi itholakalisa umbhalo, futhi indlela yokusebenza okuzenzakalelayo ukuze ikhiqize izinhlelo zakho zokusebenza ze-agent.

Njengoba sihlolwe, ukunikela lezi zokusebenza ze-next-generation kubaluleke uma ungenza isakhiwo se-AI kanye ne-toolchain efanelekayo ukuvikela ama-agents akho.

Ku-Bright Data, mission yethu kuyimpendulo enhle: ukwenza i-AI enhle kakhulu, enhle kakhulu, futhi engatholakali kakhulu bonke, emhlabeni wonke. Ngesikhathi esilandelayo-ukugcina ukujabulela, ukujabulela, futhi ukwakha futha ye-AI.