Ngaphandle kwePrototype: 15 izifundo ezinzima ukuba i-Ship Production-Ready AI Agents

Umzekelo we-AI-Engineers kunye ne-Builders Oku kukuvula ngokuvamile ngeengxaki ezincinane zePython kunye ne-ChatGPT API key. Ngaba ukongezelela iinyathelo ezininzi ze-context, i-hit run, kunye nokufunda ukuba kuxhomekeke ngokupheleleyo. Emva koko, ufuna ukuba uyenze into efanelekileyo. Emva koko, ngokufanelekileyo. Emva koko, ngaphandle kwakhona. Yeyona xa ufumana ukuba akuyona kuphela kwi-LLM. Uyafunda i-agent. Ndidlala ngonyaka lokugqibela ukuhanjiswa kwe-scripts kunye ne-wrappers, ukuhanjiswa kwi-LangChain chains ezininzi njengoko i-house ye-cards kunokuba kwi-systems, kwaye ngokuqinisekileyo ndingathanda, " ” Yintoni abantu ngokwenene zithunyelwe le izinto? Ndithanda iimfome ezibonakalayo ngokucacileyo kwi-theory kodwa zithuba ngexesha iinkcukacha zayo ziye ziye zibonakalisa. Ndicebisa i-agents ezifanelekileyo kwi-notebook kwaye zithembisa ngokucacileyo kwi-production. Ndicinga ukuba i-repo elandelayo, i-tool elandelayo, i-framework elandelayo iya kuthetha yonke into. Yintoni. Yintoni ebonakalisa ngempumelelo, ukunceda izinto, kunye nokufunda into efanelekileyo phantsi komthamo, akukho into efanelekileyo kwi-LinkedIn. Ukuba unemibuzo efanayo, kufuneka uqhagamshelane nawe. Le nqakraza i-destillation ye-clarity ebonakalayo Yenzelwe njenge-guide ye-pragmatic yokuhamba kwi-API wrappers kunye ne-chain ukuya kwi-stable, i-AI systems. Uhlobo 1 - Get the Foundation Right I-agent prototypes ezivela ngokushesha: iimpawu ezininzi, iingcebiso ezininzi, kwaye oku, iyasebenza. Uyakwazi ukufumana, "Ukuba kufanelekile, ngoko ke ukutshintshela izinto?" Okokuqala, yonke into ibonakalayo: i-agent ibonakalisa, isebenza i-code, kwaye ibonakalisa njengoko kufuneka. Kodwa ngexesha lokubhalisa i-model, ukuqala kwinkqubo, okanye ukongeza i-interface entsha, izinto zihlala. I-agent ibonakalisa, ebonakalayo, kwaye kuthatha ukuphazamiseka. : ukulawulwa kwamemori ezincinane, iindleko hardcoded, akukho ukuxhaswa kweSession, okanye ingxelo ingxelo rigid. Ngokuvamile, ingxaki ayikho logic okanye iingcebiso; kuhle kakhulu Oku kubandakanya iingcango ezine eziphambili eziza kukunceda ukuvelisa isakhiwo eluhlaza, isakhiwo apho i-agent yakho inokukhuthaza kwaye ifumaneka ngempumelelo. I-1 - I-Externalized State : The Problem Ingaba ungenza ukuba i-agent ibonakaliswa, i-crashes, i-time out, nayiphi na into. It kufuneka ukufumana ngexesha elifanelekileyo apho i-stop. I-Reproductibility: Ufuna ukudlala into efanayo kwi-test kunye ne-debugging. Ixabiso yeBonus: Kwangaphambili okanye phantsi, uya ufuna ukuqhuba iindawo ze-agent ngokuhambelana, njenge-comparing options mid-conversation okanye i-branching logic (i-Memory management yi-theme eyahlukileyo siza kuxhomekeke ngokushesha). : Qhagamshela zonke i-state ngaphandle kwe-agent, kwi-database, i-cache, i-storage layer, okanye kwifayile ye-JSON elula. The Solution : Your Checklist I-agent ifumaneka ngexesha elinye ukusetyenziswa kuphela kwi-session_id kunye ne-state ye-external (isib. ifumaneka kwi-DB okanye i-JSON). Uyakwazi ukuxhaswa kunye nokuguqula kwebhizinisi ngexesha elinye (kwakhona emva kokuguqula ikhowudi) ngaphandle kokuphazamiseka okanye ukuphazamiseka kwimveliso. I-State iyatholakala ngokupheleleyo ngaphandle kokuphazamiseka umsebenzi. I-state efanayo ingasetyenziswa kwi-agent kwi-instances ezininzi ezisebenza ngexesha le-conversation. 2 - Ukukhuthaza ulwazi : I-LLMs ayikwazanga ngokwenene. Ngaphandle kwexesha elinye, zinokukwazi ukufumana into oyifunayo, ukuxhaswa kwezinyathelo ze-conversation, ukuchitha i-thread, okanye ukuqala "ukugqithisa" iinkcukacha ezikhoyo. Ngoko ke, i-context windows zinezinto ezininzi (i-8k, i-16k, i-128k tokens) kodwa iinkcukacha ziquka: The Problem Iimodeli ibekwe ekuqaleni kunye nokugqibela, ukunciphisa iinkcukacha ebalulekileyo zangaphakathi. Iimveliso ezininzi ziquka imali ezininzi. I-limit ikhona: i-transformers isebenza kunye ne-self-attention kwi-O(n2) complexity, ngoko i-infinite context iyakwazi. Oku kuthatha kakhulu xa: Iingxowa ezininzi Izixhobo ezininzi Instructions are complex I-Agent yakho kufuneka uqhagamshelane ne-memory ye-external: ukugcina, ukufumana, ukuphefumula, kunye nokuthintela ulwazi ngaphandle kwe-model. The Solution : Common approaches I-Memory Buffer: Izixhobo ze-k ezidlulileyo. I-Prototype ye-Rapid, kodwa i-Information ezidlulileyo kwaye ayixhomekeke. I-Summarization Memory: I-History i-compresses ukuze ifumaneke ngakumbi kwi-context. I-Token ifumaneka kodwa i-risk ye-distortion kunye ne-loss ye-nuance. I-RAG (Retrieval-Augmented Generation): Izixhobo zenzululwazi ezivela kwiinkqubo ze-databases. I-scalable, i-fresh, kunye ne-verifiable, kodwa i-complex kunye ne-latency-sensitive. I-Knowledge Graphs: Ukuxhaswa kwe-facts kunye ne-entities. Elegant and explainable, but complex and high barrier to entry. : Your Checklist Zonke i-conversation history ifumaneka ngaphandle kwe-prompt kwaye ifumaneka. Izixhobo zenzululwazi zithunyelwe kwaye zithunyelwe. I-History ingakwazi ukuvelisa ngexesha elide ngaphandle kokuphumelela kwi-context window limits. 3 - Yenza i-Model Swappable : I-LLM ihamba ngokukhawuleza: I-OpenAI, i-Google, i-Anthropic, kunye nabanye abasebenza ngokugqithisileyo iimodeli zabo. Njengombhali, siyafuna ukufikelela kwimveliso ezininzi ngokukhawuleza. I-Agent yakho kufuneka uqhagamshelane phakathi kwimodeli ngokukhawuleza, okanye ngenxa yokusebenza okulungileyo okanye ixabiso eliphantsi. Problem : Solution Ukusebenzisa i-parameter model_id kwi-configs okanye i-environment variables ukucacisa i-model yokusetyenziswa. Yenza iinterfaces abstract okanye izifundo ze-wrapper ezihambelana nemodeli ngokusebenzisa i-API eyodwa. Okungenani, isetyenzise izihlangu ze-middleware ngokufanelekileyo (i-frameworks ziyafumaneka kunye ne-compromise). : Checklist Ukuguqulwa kwimodeli ayifake ikhowudi yakho okanye kuxhomekeke kwezinye iingxaki njenge-memory, i-orchestration, okanye izixhobo. Ukongezelela imodeli entsha kunceda nje ukuhlaziywa kwe-config kwaye, ukuba kufuneka, ukongezelela i-adapter layer elula. Ukuguqulwa kwimodeli ngokukhawuleza nangokugqithisileyo - ngempumelelo ukuxhaswa kwimodeli ye-models, okanye okungenani ukuxhaswa ngempumelelo kwi-model family. 4 — One Agent, Many Channels : Ngaphandle kokuba u-agent yakho uqala nge-interface eyodwa (ngokuthi, i-UI), abasebenzisi ziza kufuneka izindlela ezininzi zokuxhumana: Slack, WhatsApp, SMS, ngoko ke ngexesha le-CLI yokubhalisa. Ngaphandle kokufanelekileyo, ungenza inkqubo ebandayo. Problem : Yenza i-Unified Input Contract, i-API, okanye i-interface ye-universal apho zonke i-channels zithunyelwe. Yenza i-channel-specific logic ezahlukileyo kwi-core ye-agent yakho. Solution : Checklist I-Agent isebenza nge-CLI, i-API, i-UI, okanye nayiphi na enye interface Zonke iingxaki zihlanganisa nge-endpoint, i-parser, okanye i-schema Zonke interface usebenzisa i-input format efanayo Akukho imibuzo yobuchwepheshe ebonakalayo kwi-adapter ye-channel Ukongeza iikhangiso ezintsha kubandakanyeka nje ukudala i-adapter - akukho ukuguqulwa kwe-core agent code Umzekelo we-2 - Move Beyond Chatbot Mode Nangona kunokuba kukho umsebenzi elinye, yonke into elula, njengoko kwiiposi ze-AI influencers. Kodwa xa ukongeza izixhobo, isisombululo se-decision, kunye nezinyathelo ezininzi, i-agent iboniswa kumnqweno. Yenza i-track, ayidinga ukuba isebenze iingxaki, ayidinga ukuhanjiswa kwinqanaba elifanelekileyo, kwaye uye uye kwenziwa nje kunye neengxaki, apho "ngokuthe, yonke into ingxakiwe apho." Ukukhusela oku, i-agent kufuneka iimodeli evulekileyo yokusebenza: yintoni iye, izixhobo zayo, ngubani ukuthatha imibuzo, indlela umntu uqhagamshelane, yintoni ukuba uyenze xa kukho imiphumo embalwa. Oku kubandakanya iinkqubo ezincinane ezininzi eziza kukunceda ukwandisa i-agent yakho ngaphezu kwe-chatbot elula, ukwakha isampuli esebenzayo esebenzayo esebenzayo ngempumelelo, ukulawula iingxaki, kunye nokusebenza iimveliso ezininzi. 5 - I-Design ye-Tool Usage : Ingaba kubonakala ngokucacileyo, kodwa ama-agent ezininzi ziquka kwi-"Plain Prompting + Raw LLM output parsing." Yinto efana nokufaka i-motor yeemoto ngokugqithisa iintlobo. Xa i-LLM ibonelela i-text efanelekileyo ukuba siza kuxhomekeke nge-regex okanye i-string methods, uya kufumana iinkcukacha ezininzi: Problem I-Brittleness: Ukuguqulwa kwe-wording okanye umzekelo we-phrase inokukwazi ukuxhaswa kwe-parsing yakho, okuvelisa i-arms race epheleleyo phakathi kwe-code yakho kunye ne-unpredictability ye-model. Ukucaciswa: Isilwanyana esemgangathweni. "Call John Smith." Yintoni John Smith? Yintoni inombolo? Ukulungiselela ukuxhaswa: Ukuxhaswa kwe-coding kunokwenzeka kwaye kunzima ukuxhaswa. Yonke i-agent entsha "i-skill" inokufunda ukuxhaswa kwezinto ezininzi ze-parsing. Izixhobo ezincinane: Izixhobo ezininzi zithunyelwe ngokufanelekileyo okanye zithunyelwe izakhiwo zeedatha ezincinane nge-text elula. : Nceda umzobo ukuguqulwa i-JSON (okanye i-format eyodwa), kwaye nceda inkqubo yakho uqhagamshelane ukuqhuba. Oku kubalulekile ukuba i-LLMs zibonise intloko lomsebenzisi kwaye bafumane ukwenza, kwaye ikhowudi yakho ibonelela Ukusebenza i-function efanelekileyo nge-interface efanelekileyo. Solution Yintoni Yintoni Iinkonzo ezininzi (i-OpenAI, i-Google, i-Anthropic, njl) ziyafumaneka ngoku okanye : function calling structured output Uyakwazi ukucacisa izixhobo zakho njengezixhobo ze-JSON nge-imeyile, i-description kunye ne-parameters. I-descriptions yi-key ngenxa yokuba i-model ibekwe kwakhona. Xa uqhagamshelane neemodeli, unika le schemes ezisetyenzisiweyo kunye neengxaki. I-model ivumela i-JSON enikezela: (1) i-function yokufaka, (2) i-Parameters ngokuhambisana ne-schema Ikhowudi yakho ilawula i-JSON kwaye ilawula i-function efanelekileyo kunye neeparametre. Okungenani, i-output ye-function ingasetyenziswa kwimodeli yokuguqulwa kwe-response ye-ultimate. : Ukubhalisa izixhobo ziquka inkxaso. Ukuba ziquka, umzobo ingaba ukhethe isixhobo olungileyo. Yintoni ukuba umzobo wakho ayikwazi ukuxhasa isixhobo, okanye ufuna ukunceda? Important Ask the model to produce JSON output via prompt engineering and validate it with libraries like Pydantic. This works well but requires careful formatting and error handling. : Checklist Iingxelo zihlanganisa ngokugqithisileyo (isib. JSON) Iinkcukacha ze-tool zihlanganiswa nge-schemes (i-JSON Schema okanye i-Pydantic) I-output iyahlaziywa ngaphambi kokusebenza Iingxaki ze-format ayidla i-system (ukulawula iingxaki ze-graceful) I-LLM ibonise ukuba ibhale, ikhowudi ibhalisele ekusebenzisweni 6 - Yenza i-Control Logic kwi-Code Uninzi leengcali namhlanje ibhalisele njenge-chatbots: umdlali uthetha into, umdlali uthetha. It is a ping-pong pattern; elula kwaye ebonakalayo, kodwa enzima kakhulu. Problem: Ngokuqhelekileyo, i-agent yakho ayikwazi: Ukusebenza ngokufanelekileyo ngaphandle kwe-user prompt Ukuqhagamshela umsebenzi ngokuhambelana Plan and Sequence Izinyathelo ezininzi U-Retry wahlala iingxaki ngokucacileyo Ukusebenza kwi-background Yenza reactive kunokuba proactive. Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo: Olandelayo What you really want is an agent that thinks like a scheduler Yintoni i-agent yakho kufuneka abe: Ukulungiselela Ukudibanisa izigaba ezininzi kunye Ukuguqulwa kwimiphumo Ukuguqulwa phakathi kwezicelo Qhagamshelana nathi, nangona akukho umntu wathatha Move the control flow out of the LLM and into your system. The model can still help (e.g., decide which step comes next), but the actual sequencing, retries, and execution logic should live in code. Solution: Yinto flips umsebenzi yakho ukusuka to Umzila uye yinkuni ye-architecture emininzi, ayikho umphathi we-puppy. prompt engineering system design Nceda siqonde iindlela ezintathu ezihambisa le ngxaki. 1. Finite State Machine (FSM) Break the task into discrete states with defined transitions. What it is: Ubungakanani LLM: Ukusebenza kwindawo enye okanye ukunceda ukhethe elandelayo. Linear, predictable flows. Best for: Simple, stable, easy to debug. Pros: StateFlow, YAML configs, classic state pattern in code. Tools: 2. Directed Acyclic Graph (DAG) Yintoni: Ukubonisa umsebenzi njenge-graph - i-nodes zihlanganisa, i-rands zihlanganisa. I-LLM isebenza njenge-node okanye ukunceda ukuvelisa i-graph. Best for: Flows Branching, iminyango parallel. Flexible, visual, good for partial recomputation. Pros: LangGraph, Trellis, LLMCompiler, or DIY with a graph lib. Tools: 3. Planner + Executor Yintoni: I-agent ye-agent (okanye i-model) ibekwe kwiplano; amanye abaninzi baqhuba ngexesha elandelayo. Big model plans, small ones (or code) execute. LLM role: Modular systems, long chains of reasoning. Best for: Separation of concerns, scalable, cost-efficient. Pros: LangChain’s Plan-and-Execute, or your own planner/executor architecture. Tools: Why This Matters Uyakwazi ukufumana ukulawula umzimba we-agent Uyakwazi ukulayisha, ukucacisa, kunye nokuvavanya iinyathelo ezahlukeneyo Ungayifaka iindawo ngokuzimela okanye iimodeli ze-swap Uyakwazi ukwenza izinto ezibonakalayo kunye nezibonakalayo kunokuba zihlukile kunye nezobugcisa Checklist Agent follows the FSM, DAG, or planner structure LLM suggests actions but doesn’t drive the flow Uyakwazi ukufikelela ukuvelisa umsebenzi Umgangatho we-error ibekwe kwi-flow logic 7 — Keep a Human in the Loop Nangona izixhobo, i-control flow, kunye ne-outputs ezihlangeneyo, i-autonomy epheleleyo kunokuba i-myth. what they’re doing. They can’t be held accountable. And in the real world, they will make the wrong call (sooner or later). Problem: Ukucinga When agents act alone, you risk: deleting records, messaging the wrong person, sending money to a dead wallet. Irreversible mistakes: : violating policy, law, or basic social norms. Compliance issues Umgangatho we-strange: ukuchithwa kwezinyathelo, ukuchithwa kwezinyathelo, okanye nje ukwenza into engapheliyo. Ukukhangisa ukuxhaswa: Abasebenzisi awukwazi ukuxhaswa kwimfuneko esibonakalayo. Ukunciphisa ukuxhaswa: Xa uxhaswa, engabonakali ukuba yintoni iye kwangaphambili okanye umntu owenziwe ngempumelelo. Solution: Bring Humans Into the Loop (HITL) Treat the human as a co-pilot, not a fallback. Design your system to , , or decisions to a person when needed. Not everything should be fully automatic. Sometimes, “Are you sure?” is the most valuable feature you can build. Ukucinga ask route Ways to Include Humans Iingxowa zokusetyenziswa: Iingxowa ezininzi okanye ezininzi (isib. Ukukhuthaza, ukuchithwa, ukuhlaziywa) zinxibelelana ngokucacileyo kumntu. When the model’s confidence is low or the situation is ambiguous, route to a human for review. Escalation paths: Allow users to review and edit model responses before they’re sent. Interactive correction: Capture human feedback to improve agent behavior and train models over time (Reinforcement Learning from Human Feedback). Feedback loops: Options Override: Kufuneka abantu ukutshintsha, override, okanye re-route umxokozelo umxokozelo. Checklist Sensitive actions are confirmed by a human before execution There’s a clear path to escalate complex or risky decisions Users can edit or reject agent outputs before they’re final Logs and decisions are reviewable for audit and debugging I-Agent ibonisa indlela yokuba uqala (ngakumbi kunokwenzeka) 8 — Feed Errors Back into Context Iinkqubo ezininzi zihlala okanye zihlale xa ingxaki imiphumela. Kwi-agent ye-autonomous, nto leyo i-dead end. Kodwa ukuchitha ngempumelelo ama-errors okanye i-hallucinating kuxhomekeke kwakhona. Problem: What can go wrong: Any failure, whether an external tool error or unexpected LLM output, can break the entire process. Brittleness: I-Inefficiency: I-frequent restarts kunye ne-manual fixes i-wasted time and resources. Without awareness of its own errors, the agent can’t improve or adapt. No Learning: Errors unaddressed can lead to misleading or fabricated responses. Hallucinations: Treat errors as part of the agent’s context. Include them in prompts or memory so the agent can try self-correction and adapt its behavior. Solution: How it works: Capture error messages or failure reasons clearly. Understand the error: I-Self-correction: I-Agent ibonelela kwi-error kwaye ibonelela nge-: (1) ukucacisa kunye ne-diagnosis, (2) ukuguqula iiparamitha, ukuguqula iimfuno, okanye ukuguqula izixhobo, (3) ukuguqula umsebenzi kunye neengxaki. Detailed error info (like instructions or explanations) helps the agent correct itself better. Even simple error logs improve performance. Error context matters: Incorporate error-fix examples into model training for improved resilience. Training for self-correction: If self-correction repeatedly fails, escalate to a human (see Principle 7). Human escalation: Checklist: Errors from previous steps are saved and fed into context Retry logic is implemented with adaptive changes Iingxaki ezininzi zibonisa i-fallback kwi-human review okanye i-intervention 9 — Split Work into Micro-Agents Izixhobo zokusebenza ezininzi kwaye ezininzi, i-context window elide, kwaye i-LLM inokukwazi ukufumana i-plot. Izixhobo zokusebenza ezininzi kunye nezinyathelo ezininzi zithuthaza i-model ngaphandle kwe-sweet spot yayo, okuholela ukuxhaswa, i-tokens ezininzi, kunye nokunciphisa okunciphisa. Problem: Divide and conquer. ukusetyenziswa Iingxowa ze-Orchestrator ze-Orchestrator ze-Orchestrator ze-Orchestrator ze-Orchestrator ze-Orchestrator. Solution: small, purpose-built agents Why small, focused agents work : shorter windows keep the model sharp. Manageable context one agent, one task, zero ambiguity. Clear ownership: simpler flows mean fewer places to get lost. Higher reliability: Ukucaciswa okufanayo: unako ukucacisa ngamnye iimveliso ngalinye. when something breaks, you know exactly where to look. Faster debugging: Kukho isisombululo yokuzonwabisa ukuba ukwahlula isisombululo; ke ingxenye yokuzonwabisa, ingxenye yokuzonwabisa, kwaye umgca uya kubandakanyeka xa iimodeli zithembisa. Isisombululo elungileyo: ukuba ungenza ukubonisa umsebenzi we-agent ngexesha elinye okanye ezimbini, ngoko kunokufumana kakhulu. Checklist I-workflow epheleleyo iyisiseko ye-micro-agent calls. Each agent can be restarted and tested on its own. Uyakwazi ukucacisa umzekelo we-Agent kwi-1-2 iingxelo. Umzekelo we-3 - Ukuguqulwa kwimveliso Most agent bugs don’t show up as red errors; they show up as weird outputs. A missed instruction. A half-followed format. Something that almost works… until it doesn’t. That’s because LLMs don’t read minds. They read tokens. The way you frame requests, what you pass into context, and how you write prompts, all of it directly shapes the outcome. And any mistake in that setup becomes an invisible bug waiting to surface later. This is what makes agent engineering feel unstable: . if you’re not careful, every interaction slowly drifts off course This section is about tightening that feedback loop. Prompts aren’t throwaway strings, they’re code. Context isn’t magic, it’s a state you manage explicitly. And clarity isn’t optional, it’s the difference between repeatable behavior and creative nonsense. 10 — Treat Prompts as Code Too many projects treat prompts like disposable strings: hardcoded in Python files, scattered across the codebase, or vaguely dumped into Notion. As your agent gets more complex, this laziness becomes expensive: Problem: It’s hard to find, update, or even understand what each prompt does Akukho ukulawula inguqulelo - akukho indlela ukuyifaka into efanelekileyo, xa, okanye ngoko Optimization becomes guesswork: no feedback loops, no A/B testing And debugging a prompt-related issue feels like trying to fix a bug in a comment ikhowudi. Zibonisa isebenziswano. Ngoko ukulawula kwabo njengoko ufuna ikhowudi real: Solution: Imibuzo from your logic: store them in , , , or use template engines like Jinja2 or BAML Separate them txt .md .yaml .json with your repo (just like functions) Version them Zifumaneke: (1) Ukuphendula kwe-Unit-test ye-format, i-keywords, i-JSON validity, (2) Ukuqhuba i-evalues kwi-variations ye-prompt, (3) Sebenzisa i-LLM-as-a-judge okanye i-heuristic scoring yokubala ukusebenza Treat prompt reviews like code reviews. If a change could affect output behavior, it deserves a second set of eyes. Bonus: Checklist: I-Prompts ifumaneka ngaphandle kwe-code yakho (kuye ziye zithunyelwe ngokucacileyo) They’re versioned and diffable Zifumaneka okanye zibonwa Zifumaneka kwi-review xa kufuneka 11 - Engineer I-Context Stack We’ve already tackled LLM forgetfulness by offloading memory and splitting agents by task. But there’s still a deeper challenge: we format and deliver information to the model. Problem: how Most setups just throw a pile of role: content messages into the prompt and call it a day. That works… until it doesn’t. These standard formats often: I-Burn tokens kwi-metadata ye-redundant Struggle to represent tool chains, states, or multiple knowledge types Ukungabonakaliswa kwimodeli ngokufanelekileyo kwi-flow complex Nangona kunjalo, sincoma ukuba iimodeli "ngokufuneka nje." Yinto ayikho injini. Yinto vibes. Solution: Engineer the context. Treat the whole input package like a carefully designed interface, because that’s exactly what it is. : Here’s how : Control what gets in, how it’s ordered, and where it shows up. Everything from system instructions to retrieved docs to memory entries should be intentional. Own the full stack : Build richer, denser formats. XML-style blocks, compact schemas, compressed tool traces, even Markdown sections for clarity. Go beyond chat format : Context = everything the model sees: prompt, task state, prior decisions, tool logs, instructions, even prior outputs. It’s not just “dialogue history.” Think holistically Oku kubaluleke ngokukodwa ukuba uqhagamshelane: Ukucaciswa kwe-Information Density: Ukupakisha I-More Meaning Into Fewer Tokens I-Cost Efficiency: ukusebenza eliphezulu kwi-context size ephantsi Ukhuseleko: ukulawula kunye nokubhalisa into elandelayo explicitly signaling edge cases, known issues, or fallback instructions Error resilience: Ukukhuthaza kunokuba i-half the battle. Yaye uma ungenza ngoku, uya kuba ngexesha umnxeba wakho. Bottom line: inguqulelo 12 - Ukongeza izihlangu zokhuseleko Nangona amaxwebhu eziqhelekileyo, i-memory, kunye ne-control flow, i-agent inokukwazi ukufikelela kwi-rails. Yenza le nqaku njengoko i-insurance against the worst-case scenarios: users (or other systems) slip in instructions that hijack the agent. Prompt injection: Iinkcukacha ze-Data sensitive: Iimodeli ivimbela i-PII okanye iingcebiso ze-corporate. Iinkcukacha ze-toxic okanye ze-malware: i-hate speech, i-spam, okanye i-materials eyenziwe. I-Hallucinations: Imibuzo emangalisayo kodwa emangalisayo. Ukusebenzisana kwi-out-of-scope: i-agent iye "ngabaqhelekanga" kwaye uyenza into efanelekileyo. Akukho isisombululo esisodwa esifundisa yonke into. Ufuna : izixhobo ezininzi ezihambisa iingxaki kuzo zonke iinyanga ze-request/response cycle. defense-in-depth Quick Checklist Ukubuyekezwa kwe-user input (i-jailbreak phrases, i-intention check) Kwimeko ze-factual, imibuzo kufuneka uqhagamshelane ne-RAG context. The prompt explicitly tells the model to stick to retrieved facts. blocks PII or disallowed content. Output filter Iingxelo zihlanganisa i-citation / i-link kwi-source. Agent and tools follow the . least privilege Ukusebenzisana kwezinto ezinzima nge-HITL-approval okanye i-monitoring. Ukusetyenziswa kwezi layers njenge-DevOps ye-standard: ukuhlaziywa, ukuyifaka, kwaye ukufumana ngokhuseleko. Ngokwenza oku, ungenza i-agent "i-autonomous" ukuba i-responsibility ebonakalayo. Umzekelo we-4 - Yenza it Working Under Load Kwi-production, iingxaki ziyafumaneka ngokuqondile ngexesha elinye, kwaye ngokuvamile ungaziwa ngexesha elide, ngexesha elide. Inqaku leyo ibekwe ekubunjweni lwezobugcisa ukulawula i-agent yakho ngokugqithisileyo, ukuqinisekisa ukuba yonke into isebenze ngempumelelo. . From logs and tracing to automated tests, these practices make your agent’s behavior clear and dependable, whether you’re actively watching or focused on building the next breakthrough 13 - Trace Umzila Wokugqibeleleyo Wokugqibela : Iingcali ziya kubonakale ngempumelelo ngexesha lokuvelisa, ukuhlaziywa, okanye ngexesha lokusebenza okuzenzakalelayo. Ukuxhaswa kwimeko ezininzi kunokufumana iiyure ezininzi ukuxhaswa iifayile kunye nokugqiba iifayile. Ukuba uyenze izisombululo ezininzi ezifana nokugcina i-state ngaphandle kunye nokugqiba iifayile kwi-context, uya kufutshane. Kodwa kunjalo, ukuxhaswa kwe-debugging efanelekileyo ukusuka ekuqaleni kunceda izifo zayo ezininzi. Problem : Log the entire journey from the user request through every step of the agent’s decision and action process. Individual component logs aren’t enough; you need end-to-end tracing covering every detail. Solution : Why this matters : Quickly identify where and why things went wrong. Debugging : Spot bottlenecks and improvement opportunities. Analytics Ukubuyekezwa Kwekhwalithi: Ukubuyekeza njani imibuzo emzimbeni. I-Reproductibility: I-Recreate any session ngokunyaniseka. I-Audit: Ukugcina i-record epheleleyo yeengxaki kunye neengxaki ze-agents. Minimum data to capture Input: Isicelo lomsebenzisi kunye neengxaki ezidlulileyo. : Key variables before each step. Agent state : Full prompt sent to the LLM (system instructions, history, context). Prompt LLM Output: Ukusabela Raw ngaphambi kokusabela. : Tool name and parameters invoked. Tool call Umzekelo we-Tool: Umzekelo we-Tool okanye umzekelo we-Error. : Next steps or responses chosen. Agent decision I-Metadata: I-Timing, i-Model Info, i-Cost, i-Code, kunye ne-Prompt Version. Ukusetyenziswa izixhobo zokusebenza ezikhoyo apho kunokwenzeka: LangSmith, Arize, Weights & Biases, OpenTelemetry, njl. Kodwa okokuqala, uqinisekisa ukuba unayo izifundo zihlanganisa (bheka Umgangatho 15). : Checklist Zonke iingxaki zihlanganiswa ngokugqithisileyo. I-logs ifumaneka yi-session_id kunye ne-step_id. I-Interface yokufunda i-call chains epheleleyo. Umthamo ukuguqulwa ngokupheleleyo ngexesha elinye ngexesha. 14 - Ukuvavanya zonke iimveliso : Ngoku, i-agent yakho ingathanda ukuqala: ifumaneka, ngoko ke ngexabiso efanelekileyo. Kodwa ngoko ufumane ukuba uya kukusebenza emva kweemva? Imibuzo kwi-code, i-datasets, iimodeli ze-base, okanye amaxwebhu angakwazi ukuxhaswa ngexabiso okanye ukunciphisa ukusebenza. Izindlela zokucwangcisa ezivamile ziquka zonke iingxaki ze-LLMs: Problem : performance drops over time without code changes due to model or data shifts Model drift Ukucaciswa okusheshayo: Ukucaciswa okusheshayo ezincinane kunokukwazi ukucaciswa okuphambili I-non-determinism: I-LLM inikeza imibuzo ezahlukeneyo kwi-input efanayo, ukutshintshela izifundo ze-exact-match Iingxaki ezininzi ezidlulileyo: Ngaphandle kweengxaki ezincinane, ama-bug ziyafumaneka kakhulu Umthombo we-butterfly: Izibuyekezo ze-cascade ezidlulileyo kwiinkqubo I-Hallucinations kunye nezinye i-LLM-specific risks : Zifumaneka i-strategy ye-testing enzima kunye ne-classic software testing kunye ne-LLM-focused quality checks: Solution Ukucaciswa kwinqanaba ezininzi: ukucaciswa kwinqanaba / imiqondiso, ukucaciswa, kunye ne-end-to-end epheleleyo Ukukhangisa umgangatho kwimveliso ye-LLM: umgangatho, umgangatho, ubunzima, umgangatho kunye nokhuseleko Ukusetyenziswa kwe-regression, usebenzisa i-gold datasets kunye ne-outputs ezidlulileyo okanye i-acceptable result ranges Ukuhlolwa okuzenzakalelayo kwaye zihlanganisa kwi-CI / CD pipelines Ukusetyenzisa abantu kwi-evaluations eziphambili okanye ezininzi (umntu-in-the-loop) I-Iteratively testing and refining prompts ngaphambi kokusebenza Ukuhlolwa kwinqanaba ezahlukeneyo: components, prompts, chains/agents, kunye nophuhliso zokusebenza epheleleyo : Checklist I-Logic yi-modular and thoroughly tested individually and in combination Umgangatho we-output iyahlaziywa kwiinkcukacha ze-benchmark Iimvavanyo zihlanganisa iimeko eziqhelekileyo, iimeko ze-edge, iingxaki kunye neengxaki ze-malware Ukuvuthwa kwamandla kwi-input ye-noisy okanye i-adverse Zonke iimveliso zithunyelwe iimvavanyo ze-automatic kwaye zihlanganiswa kwimveliso ukuze zibonise i-regression engapheliyo 15 - Own the Whole Stack This principle ties everything together, it’s a meta-rule that runs through all others. Kwiintsuku, kukho izixhobo ezininzi kunye nezixhobo zokuphathelela cishe nayiphi na umsebenzi, nto leyo elungileyo ngenxa yokushesha kunye nokugqitheka kwe-prototyping - kodwa iyiphi na ingxaki. Ukuphathelela kakhulu kwi-framework abstractions ikakhulu kunokuba kunceda ukuxhaswa kwe-flexibility, ukulawula, kwaye ngexesha elinye, ukhuseleko. Oku kubalulekile kakhulu kwi-agent development, apho kufuneka ukulawula: The inherent unpredictability of LLMs I-Logic Complex eyenziwe nge-transitions kunye ne-self-correction Ukusetyenziswa kwinkqubo yakho nokuguqula nokuguqula ngaphandle kokubhalisa iingxaki zangaphambili : they dictate how your agent should behave. This can speed up prototyping but make long-term development harder to manage and customize. Frameworks often invert control Kwimeko ezininzi iingcebiso ezaziwayo ziyafumaneka kwi-off-the-shelf tools. Kodwa ngexesha, ukwakha i-logic ye-core ngokucacileyo kuthatha inxaxheba elifanayo kwaye inikeza ukucacileyo kakhulu, ukulawula, kunye ne-adaptability. Ngaphandle kwalokho, ukufikelela ngokupheleleyo kunye nokuthintela yonke into ukusuka kwakhona kunokwenzeka, kunye nokufanelekileyo. Yintoni Key Njengomatshini, unayo ukhethe ngexesha lokufunda kwi-frameworks kwaye ngexesha lokufumana ukulawula ngokupheleleyo, ukufumana ngokupheleleyo iingxaki ezixhomekeke. Ukucinga : iintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo zeintlobo. Remember Ukucinga Ukwakhiwa kwe-agent ye-LLM akugqiba kuphela ukulanda i-API. I-Design ye-system enokufumana i-real-world messiness: amaxabiso, i-state, amaxabiso ze-context, amaxabiso ze-input, kunye nezidingo ezintsha. I-15 iingcebiso ezidlulileyo ezidlulileyo ayikho i-theory, ziye izifundo ze-batch-tested ezivela kwi-trance. Ziza kukunceda ukwandisa i-scripts ezincinane kwi-stable, i-escalable, kunye neengcebiso ze-maxi-maxi-maxi-maxi-maxi-maxi-maxi-maxi. Kwimeko, iprojekthi yakho, izicwangciso zakho, kunye nokuveliswa yakho. Kodwa ucebisa: i-LLM inamandla, kodwa kuphela ingxenye yeenkqubo epheleleyo. Iingcali yakho njenge-engineer kuyinto yokuvelisa inqubo, ukulawula i-complexity, kunye nokuphucula yonke into ngokushesha. Ukuba unayo into eyodwa, nceda uye: i. Ngenxa yokuba yindlela kuphela ukufika kwi-”owow, it replied!” kwi-”yes, it continues to work.” slow down, build solid foundations, and plan for the long hau Qhagamshelana nathi, ukuhlolwa kunye nokufunda. Kwaye asisekuseni, abantu kwi-lyric ayikho umngcipheko, bafumane i-agent yakho ebonakalayo kunye ne-effective. Yinto akuyona lokugqibela. Kuyinto kuphela lokuqala yokwakha ama-agents ezifanelekileyo. Ukukhangisa ukuvelisa i-audience yakho njenge-Tech Professional? The Tech Audience Accelerator is the go-to newsletter for tech creators serious about growing their audience. You’ll get the proven frameworks, templates, and tactics behind my 30M+ impressions (and counting). https://techaudienceaccelerator.substack.com/embed?source=post_page-----e58139d80299---------------------------------------&embedable=true