Ngaphansi kwe-prototype: 15 izifundo ezinzima zokwenza ama-ship-production-ready AI agents

Umhlahlandlela we-AI-Engineers ne-Builders Ngokuvamile, kuqala ngezinyathelo eziningana ze-Python kanye ne-ChatGPT API key. Ingabe ukongeza amayunithi amancane, uxhumane, futhi uhlasele ukuthi kusabela ngokugcwele. Emva kwalokho, uzodinga ukwenza okufanayo. Emva kwalokho, ngempumelelo. Emva kwalokho, ngaphandle kwami. Kuyinto lapho uzothola ukuthi akuyona kuphela LLM. Ingabe ukwakha i-agent. Ngitholile ngonyaka esidlulile ukudibanisa i-scripts kanye ne-wrappers, ukudibanisa i-LangChain amakethe ebonakalayo emkhakheni ye-cards kunezinhlelo, futhi ngokushesha ukhangela, " ” Ungayifaka kanjani abantu ngokwenene le izinto? Ngitholela imizamo okuyinto zibonakalayo emangalisayo kodwa zihlukile lapho abasebenzisi zayo ziye zibonakali. Ngitholile ama-agents ezisebenzayo ngokushesha ku-notebook futhi zihlukile ngokushesha ekukhiqizeni. Ngitholile ukuthi i-repo elandelayo, i-tool elandelayo, i-framework elandelayo izixazulule konke. Yini kungekho. Yini kunikezela ngempumelelo, ukunciphisa izinto, futhi ukuncintisana into efanelekayo esebenza ngaphansi kwezimpendulo, akuyona okuhlobene ku-LinkedIn. Uma unemibuzo efanayo, kulandelwa kuwe. Umhlahlandlela wesitimela kuyinto distillation of that hard-earned clarity Yenziwe njenge-guide esebenzayo yokuhamba kusuka ku-API wrappers kanye nemichiza ku-stable, controlable, scalable AI systems. Isigaba 1 - Get the Foundation Emuva I-agent prototypes yokuqala ikakhulukazi ifakwe ngokushesha: ezinye izici, ezinye izimpendulo, futhi voilà, kusebenza. Uma ungathanda, "Uma kungenziwa, ngoko ke ukunciphisa izinto?" Okokuqala, konke kubalulekile: isivakashi isivakashi, isivakashi isivakashi, futhi isivakashi ngokufanelekileyo. Kodwa uma ushiye imodeli, ukuguqulwa kwekhwalithi, noma ukwengeza isixhumanisi entsha, izinto zihlukile. Isivakashi isivakashi isivakashi isivakashi, isivakashi, futhi kunzima ukuguqulwa. Imininingwane ezingenalutho ye-memory, ama-hardcoded values, akukho-session persistence, noma i-rigid input point. Ngokuvamile, ingcindezi akuyona logic noma imibuzo; kuyinto enhle kakhulu Kulesi isigaba iveza izinsizakalo ezine ezisebenzayo ukwakha isakhiwo se-rock-solid, isakhiwo lapho idivayisi yakho angakwazi ukukhula futhi ukucindezeleka ngempumelelo. I-1 - I-Externalized State : The Problem Ungenza ukuguqulwa uma umphakeli ukuphazamiseka, i-crashes, i-time out, noma iyiphi na. It kufuneka ukuthatha esifanele lapho i-stop. I-Reproductibility: Ufuna ukudlala okufanayo ukuhlolwa nokupholisa. Umthamo weBonus: Okusheshayo noma ngempumelelo, uzothola izigaba ze-agent ngokuhambisana, njenge-comparing options mid-conversation noma i-branching logic (I-Memory management iyinhlangano eyahlukile singatholakala ngokushesha). : Xhumana zonke izimo ngaphandle kwe-agent, ku-database, i-cache, i-storage layer, noma ngisho ifayela le-JSON elula. The Solution : Your Checklist I-agent uqala ngezinyathelo ezimbalwa ngokusebenzisa kuphela i-session_id kanye ne-state ye-external (isib. I-DB noma i-JSON). Ungahambisa kanye nokuguqulwa kwebhizinisi ngexesha elinye (ngezinye emva kokuguqulwa kwe-code) ngaphandle kokubili ukuthuthukiswa noma ukuphazamiseka kwebhizinisi. I-State iyatholakala ngokugcwele ngaphandle kokugcina umsebenzi. I-state efanayo ingatholakala kumakhasimende amaningi abalandeli abalandeli abalandeli abalandeli abalandeli. I-2 - Ukukhishwa kweKnowledge : I-LLM ayikwazanga ngokwenene. Ngaphandle kwe-session eyodwa, zingenakuthanda into etholakalayo, zihlanganisa izigaba zokuxhumana, zihlanganisa i-thread, noma zihlanganisa "ukushintshwa" ama-details asikho. Ngokuthi, ama-context windows zihlanganisa (i-8k, i-16k, i-128k i-tokens) kodwa ama-problems zihlanganisa: The Problem Umhlahlandlela wokusekelwe ekuqaleni kanye nokugqibela, ukunciphisa imibuzo ebalulekile esisodwa. I-Token engaphezu kwamanani engaphezu kwamanani. I-limit ikhona: i-transformers isebenza ngokucindezeleka ku-O(n2) ukuxuba, ngakho-ke iphakheji okungenani ayikwazi. Okuhlobene kakhulu lapho: Imibuzo eside Izici zihlanganisa Izicelo zihlanganisa I-Agent yakho kufanele usebenza ne-memory e-external: ukugcinwa, ukufumana, ukuphefumula, nokuvakasha ulwazi ngaphandle kwe-model ngokuvamile. The Solution : Common approaches I-Memory Buffer: ibhekwa imiyalezo yokuqala ye-k. I-Prototype ye-Rapid, kodwa ivame ulwazi olusha futhi akufanele ukuhlaziywa. I-Summarization Memory: I-history ye-compress to fit more in context. Inikeza ama-tokens kodwa inesibopho sokugqoka nokunciphisa. I-RAG (Retrieval-Augmented Generation): Inikeza ulwazi kusuka ku-databases ezingaphandle. I-scalable, i-fresh, ne-verifiable, kodwa i-complex ne-latency-sensitive. I-Knowledge Graphs: Ukuxhumana okuzenzakalelayo phakathi kwebhizinisi nama-entities. Elegant ne-explainable, kodwa i-complex ne-high barrier to entry. : Your Checklist Konke idatha ye-conversation ibhekwa ngaphandle kwe-prompt futhi iyatholakala. Izinto ezaziwayo zenzelwe futhi zithunyelwe. I-History ingakwazi ukukhula ngempumelelo ngaphandle kokuthintela ama-context window limits. 3 - Yenza imodeli swappable : I-LLM ihamba ngokushesha: I-OpenAI, i-Google, i-Anthropic, nezinye abasebenza ngokushesha amamodeli zabo. Njengoba ama-engineers, sincoma ukufinyelela kwezi zokusebenza ngokushesha. I-agent yakho kufanele ukuguqulwa phakathi kwamodeli ngokushesha, noma ukusebenza okuphumelela noma izindleko ezingaphakeme. Problem : Solution Ukusebenzisa i-parameter model_id ku-configs noma ama-environment variables ukucacisa ukuthi isampula kusetshenziswe. Yakhelwe interface abstract noma izigaba wrapper ezivela amamodeli ngokusebenzisa i-API eyodwa. Okungenani, isetshenziswe izindandatho ze-middleware ngokuzimela (i-frameworks iyatholakala nge-compromise). : Checklist Ukuguqulwa kwemodeli akufanele ukuphazamiseka ikhodi yakho noma ukuphazamiseka kwezinye izingxenye, njenge-memory, i-orchestration, noma izixhobo. Ukongezelela imodeli entsha kuncike nje ukuguqulwa kwe-config futhi, uma nezidingo, ukongezelela i-adapter layer elula. Ukuguqulwa kwama-models ngokushesha futhi ngokushesha - okungenani ukweseka iyiphi imodeli, noma okungenani ukuguqulwa kalula ngaphakathi kwama-model family. I-4 - I-One Agent, I-Many Channels : Ngemuva kokufinyelela kwe-agent yakho nge-interface eyodwa (ngokuthi, i-UI), abasebenzisi akufanele ngokushesha izindlela ezininzi zokuxhumana: Slack, WhatsApp, i-SMS, noma ngisho i-CLI yokubuyisa. Ngaphandle kokufinyelela lokhu, ungenza uhlelo oluthile, elinganiselwe. Problem : Yenza i-Unified Input Contract, i-API, noma i-interface ye-universal enikezelwe kumazwe ngamunye. Hlola i-channel-specific logic eyahlukile emzimbeni wakho. Solution : Checklist I-Agent isebenza nge-CLI, i-API, i-UI, noma ezinye interface Zonke ukufinyelela funnel nge endpoint kuphela, parser, noma schema Konke interface usebenzisa elifanayo inguqulo format Akukho logic yebhizinisi ibhizinisi ngaphakathi noma enye i-channel adapter Ukongezelela ama-channels ezintsha kuncike kuphela ukubhalisa i-adapter - akukho ukuguqulwa kwe-core agent code Isigaba 2 – Move Beyond Chatbot Mode Nangona kunezinto eyodwa, konke kulula, njenge-inthanethi ye-I.I. Influencers. Kodwa uma ukongeza izixhobo, isisombululo se-decision logic, kanye nezinyango ezininzi, umeluleki uqhuba ingozi. Yenza i-track, ayikwazi ukwenza kanjani imiphumela, ayizange ukubhuka isixhobo olufanelekayo, futhi ushiye ngokulandelanayo ngokulandelana nezitolo, lapho "ngokuthi, konke kubonakala kubhalwe khona." Ukuze ukunceda lokhu, umdlavuza ungenza umzila oluthile yokusebenza: okufanayo, izixhobo zayo, umuntu ukuthatha izixazululo, indlela abantu zihlanganisa, futhi yini ukwenza uma izinto zihlanganisa. Kulesi isigaba esifundisa izinsizakalo ezincinane eziza kukunceda uxhumane u-agent yakho ngaphandle kwe-chatbot efanelekileyo, ukwakha umzila wokusebenza olusebenzayo owaziwa ukusebenzisa izindlela, ukulawula amafutha kanye nokusebenza kwezimfuneko ezinzima. 5 - Design Ukuze Usebenzisa Izixhobo : Kungase kubonakala ngokuvamile, kodwa ama-agents eziningi zihlanganisa ku-"Plain Prompting + Raw LLM output parsing." Kuyinto njengokufunda ukuguqulwa kwe-motor ye-auto ngokuguqulwa kwe-bolts ngokufanele. Uma ama-LLM akuthumela ingxelo olulodwa okuyinto siza kuxazulula nge-regex noma i-string methods, unemibuzo eminingi: Problem I-Brittleness: Ukuguqulwa okungenani kwekhwalithi noma ukubuyekezwa kwe-phrase kungabangela ukucubungula kwakho, okwenza isitimela sokuvamile phakathi kwe-code yakho ne-impredictability ye-model. I-ambiguity: I-language natural is vague. “Call John Smith.” I-John Smith? Yini idilesi? Ukulungiselela ukucindezeleka: Ukucindezeleka ikhodi kubaluleke futhi kubaluleke ukucindezeleka. Yonke i-agent entsha "i-skill" inikeza ukucindezeleka izinhlelo ezininzi. Izinzuzo ezincinane: Kubalulekile ukubiza izixhobo eziningana ngokuthembeka noma ukunikezela izakhiwo zamadokhumenti ezinzima nge-text elula. : Hlola imodeli ukuguqulwa i-JSON (noma enye ifomati eyenziwe), futhi sicela inkqubo yakho ukulawula ukuqhuba. Lokhu kubalulekile ukuba i-LLM ibonise intuthuko lomsebenzisi futhi ukhethe ukwenza, futhi ikhowudi yakho ukunakekelwa kusebenza umsebenzi olufanele ngokusebenzisa isixhumanisi olufanele. Solution Yini Indlela Umphakeli we-OpenAI, i-Google, i-Anthropic, njll. noma : function calling structured output Ungafakwa izixhobo zakho njengama-JSON Schemes nge-imeyili, isifinyezo, kanye nama-parametres. Izifinyezo zihlanganisa ngenxa yokufakelwa kwama-model. Ngemuva kokufaka kwama-model, uzokunikeza lezi zokusebenza izinhlelo kanye ne-prompt. The model returns JSON specifying: (1) the function to call, (2) Parameters according to the schema I-code yakho ibhalisele i-JSON futhi ibhalisele i-function efanelekayo nge-parameters. Okungenani, imiphumela ye-function ingathunyelwa emadolobheni lokugqibela yokuguqulwa kwe-response. : Izincazelo zokusebenza zihlanganisa ingxenye yokuhamba. Uma zihlanganisa, imodeli ingathola isixhobo oluthile. Yini uma imodeli yakho ayikwazi ukuxhuma umsebenzi, noma ufuna ukunceda? Important Thola imodeli ukukhiqiza imiphumela ye-JSON ngokusebenzisa ingcindezi okusheshayo nokuvumela nge-libraries efana ne-Pydantic. Lokhu kusebenza kahle kodwa kufuneka ukucubungula okuhlobene nokuthuthukisa imiphumela. : Checklist Izimpendulo zihlanganisa kakhulu (isib. JSON) I-Instrument Interfaces ifakwe nge-schemes (i-JSON Schema noma i-Pydantic) Ukusuka ku-validated ngaphambi kokusebenza Imibuzo ye-format akufanele ukuphazamiseka kwekhwalithi (ukudluliselwa kwekhwalithi ye-graceful) I-LLM ibonise ukuthi isebenze, ikhowudi ibhalanise isebenze I-Put Control Logic ku-Code Uninzi lwezinkimbinkimbi namhlanje izivakashi ezifana ne-chatbots: umdlali usho, umdlali ushiye. Kuyinto isampula se-ping-pong; elula futhi ebonakalayo, kodwa enzima kakhulu. Problem: Ngokusebenza okuhle, umphathi wakho ayikwazi: Ukusebenza ngokuvamile ngaphandle kwe-user prompt Ukusebenza umsebenzi ngokuhambisana Plan futhi sequence izinyathelo eziningi U-Retry wahlanganyela iminyango emangalisayo Ukusebenza kwi-background Yenza reactive kunokuba proactive. 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 Ngokuvamile, idivayisi yakho kufanele abe: Ukulungiselela Ukwakhiwa kwezinyathelo eziningana Ukuguqulwa kokuphumula Ukuguqulwa phakathi kwezidingo Thola ukusebenza, ngisho lapho umuntu akuyona 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: This flips your job from to . The model becomes one piece of a broader architecture, not the puppet master. prompt engineering system design Thola thwebula izindlela ezintathu ezimbonini zihlanganisa lokhu ukubuyekeza. 1. Finite State Machine (FSM) Break the task into discrete states with defined transitions. What it is: Acts within a state or helps pick the next one. LLM role: Best for: Linear, izimpendulo zokusebenza. Simple, stable, easy to debug. Pros: Izinsiza: StateFlow, YAML configs, isampula yesimo se-classic ku-code. 2. Directed Acyclic Graph (DAG) Represent tasks as a graph — nodes are actions, edges are dependencies. What it is: Acts as a node or helps generate the graph. LLM role: Branching flows, parallel steps. Best for: Flexible, visual, good for partial recomputation. Pros: LangGraph, Trellis, LLMCompiler, or DIY with a graph lib. Tools: 3. Planner + Executor One agent (or model) builds the plan; others execute it step by step. What it is: Big model plans, small ones (or code) execute. LLM role: Modular systems, long chains of reasoning. Best for: Imibuzo: Ukuhlobisa imibuzo, i-scalable, i-cost-effective. LangChain’s Plan-and-Execute, or your own planner/executor architecture. Tools: Why This Matters You gain control over the agent’s behavior You can retry, debug, and test individual steps Ungayifaka izingxenye ngokuzimela noma ukuguqulwa ama-models Ungayenza izinto ezibonakalayo futhi zibonakalayo kunokuba zihlukile futhi ezibonakalayo Checklist Agent follows the FSM, DAG, or planner structure LLM suggests actions but doesn’t drive the flow You can visualize task progression Ukusebenza kwe-Error ku-Flow Logic 7 — Keep a Human in the Loop Even with tools, control flow, and structured outputs, full autonomy is still a myth. LLMs don’t Ngiyazi. Akukwazi ukuchofoza. Futhi ehlabathini lokwenene, bafuna ukubheka okungenani (ngcono noma ngempumelelo). Problem: Ngena ngemvume When agents act alone, you risk: Imiphumela ye-irreversible: ukusha ama-records, ukuxhumana ne-person engabikho, ukuxhumana ne-money ku-wallet ebomvu. : violating policy, law, or basic social norms. Compliance issues skipping steps, hallucinating actions, or just doing something no human ever would. Weird behavior: : users won’t rely on something that seems out of control. Broken trust : when it breaks, it’s unclear what went wrong or who owns the mess. No accountability Solution: Bring Humans Into the Loop (HITL) Thola umntu njengoba co-pilot, akuyona umngciwane. Design uhlelo yakho ukuze , , or Izinhlelo zokusungula umntu lapho kuyimfuneko. Akukho konke kufanele kube ngokuphelele okuzenzakalelayo. Ngezinye izikhathi, "Yini sicela?" kuyinto umsebenzi enhle kakhulu ungakwazi ukwakha. pause ask route Ways to Include Humans Critical or irreversible actions (e.g., sending, deleting, publishing) require explicit human confirmation. Approval gates: 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: Izimpendulo ze-feedback: Ukukhishwa kwe-feedback yabantu ukuze kubuyekeze isebenziswano se-agent kanye nokuhlola amamodeli ngokushesha (Reinforcement Learning from Human Feedback). Enable humans to interrupt, override, or re-route the agent’s workflow. Override options: Checklist Ukusebenza okucindezelekayo kuboniswe umntu ngaphambi kokusebenza There’s a clear path to escalate complex or risky decisions Umsebenzisi angakwazi ukuguqulwa noma ukuguqulwa ama-agent outputs ngaphambi kokugcina Logs and decisions are reviewable for audit and debugging The agent explains it made a decision (to the extent possible) why 8 — Feed Errors Back into Context Most systems crash or stop when an error happens. For an autonomous agent, that’s a dead end. But blindly ignoring errors or hallucinating around them is just as bad. Problem: What can go wrong: I-Brittleness: Yonke inkinga, noma isisindo se-tool ye-external noma isisindo se-LLM engathandeki, ingangena inqubo ephelele. Frequent restarts and manual fixes waste time and resources. Inefficiency: No Learning: ngaphandle kokuthunyelwe kwama-error yayo, umeluleki angakwazi ukuguqulwa noma ukuguqulwa. I-Hallucinations: Ama-Errors engatholakali kungabangela izimpendulo ezingenalutho noma zihlanganisa. 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: The agent reflects on the error and tries to fix it by: (1) detecting and diagnosing the issue, (2) adjusting parameters, rephrasing requests, or switching tools, (3) retrying the action with changes. Self-correction: 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: I-escalation ye-human: Uma i-self-correction isixazululwa ngokuvamile, isixazululwa ku-humane (bheka umthetho we-7). Checklist: Errors from previous steps are saved and fed into context Retry logic is implemented with adaptive changes Repeated failures trigger a fallback to human review or intervention 9 - Ukusabalalisa umsebenzi ku-micro-agents Izixhobo zokusebenza amakhulu futhi emangalisayo, i-context window elide, futhi i-LLM inesibopho sokuthintela i-plot. Izixhobo zokusebenza ezihlangene nezinyathelo ezingu-dozen zithintela i-model ngaphandle kwe-sweet spot, okuholela ukuxuba, i-token eyenziwe, kanye nokunemba okunciphisa. Problem: Divide and conquer. Use I-Orchestrator ye-Orchestrator ye-Orchestrator ye-Orchestrator ye-Orchestrator ye-Orchestrator ye-Orchestrator ye-Orchestrator. Solution: small, purpose-built agents Why small, focused agents work I-Manageable Context: Izindwangu ezincinane zihlanganisa imodeli. one agent, one task, zero ambiguity. Clear ownership: simpler flows mean fewer places to get lost. Higher reliability: you can unit-test each agent in isolation. Easier testing: when something breaks, you know exactly where to look. Faster debugging: Kwesimo se-magic yokufaka i-logic; Kuyinto ingxenye yobugcisa, ingxenye yobugcisa, futhi umugqa izihlangu izihlangu ngokushesha lapho amamodeli zithuthukisa. Isisekelo esihle: Uma ungenza ukubonisa umsebenzi we-agent eminyakeni noma amabili, kungenzeka ukuthi kusebenza kakhulu. Checklist The overall workflow is a series of micro-agent calls. Konke agent kungenziwa reboot futhi test ngokulinganayo. Ungatholisa isifinyezo se-Agent ku-1-2 izixazululo. Part 3 – Stabilize Behavior Uninzi le-agent bugs akuyona njenge-errors ebomvu; akuyona njenge-outputs emangalisayo. Isinyathelo esithathwe. Isinyathelo esifundeni esifundeni. Isinyathelo esebenzayo ... kuze kube. That’s because LLMs don’t read minds. They read tokens. Ukulungiselela kanjani imibuzo, okuholela ku-context, kanye nokufaka imibuzo, konke okuhlobisa ngqo imiphumela. Futhi unemibuzo yayo ekusungulweni ivela ingxubevange okungaziwayo ekukhanyeni ngemva kokufika. Lokhu kuncike ingcindezi we-agent ngokufanele: . 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 There’s no version control — no way to track what changed, when, or why Optimization becomes guesswork: no feedback loops, no A/B testing Futhi ukuguqulwa kwebhizinisi ezinxulumene ne-prompt kusebenza njengokufunda ukuguqulwa kwebhizinisi e-comment code. They define behavior. So manage them like you would real code: Solution: Prompts are Thola kwabo kusuka ku-logic yakho: zihlanganisa ku-txt, .md, .yaml, .json noma usebenzisa izinjini zamasampuli ezifana ne-Jinja2 noma i-BAML with your repo (just like functions) Version them : (1) Unit-test responses for format, keywords, JSON validity, (2) Run evals over prompt variations, (3) Use LLM-as-a-judge or heuristic scoring to measure performance Test them Ukwelashwa imibuzo okusheshayo njenge imibuzo ye-code. Uma ukuguqulwa kungase kuthatha imibuzo ye-output, kubalulekile isibuko sesibili. Bonus: Checklist: Prompts live outside your code (and are clearly named) They’re versioned and diffable They’re tested or evaluated They go through review when it matters 11 — Engineer the Context Stack We’ve already tackled LLM forgetfulness by offloading memory and splitting agents by task. But there’s still a deeper challenge: Thola futhi ukunikezela ulwazi ku-model. Problem: Indlela 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: Burn tokens on redundant metadata Struggle to represent tool chains, states, or multiple knowledge types Fail to guide the model properly in complex flows And yet, we still expect the model to “just figure it out.” That’s not engineering. That’s 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 I-Own the full stack: Ukulawula ukuthi inikeza, indlela yisiphiwa, futhi lapho ibonisa. Yonke into kusuka ku-system instructions kuya ku-docs ebonakalayo kuya ku-memory entries kufanele yenzelwe. Thola ngaphandle kwe-chat format: Yakhelwe ama-formats amancane, amancane. Ama-blocks e-XML-style, ama-schemes amancane, ama-tool traces amancane, ngisho ama-Markdown sections ukuze kube lula. : Context = everything the model sees: prompt, task state, prior decisions, tool logs, instructions, even prior outputs. It’s not just “dialogue history.” Think holistically This becomes especially important if you’re optimizing for: packing more meaning into fewer tokens Information density: high performance at low context size Cost efficiency: controlling and tagging what the model sees Security: explicitly signaling edge cases, known issues, or fallback instructions Error resilience: Ukubuyekezwa kune-half of the battle. is the other half. And if you’re not doing it yet, you will be once your agent grows up. Bottom line: Context engineering 12 — Add Safety Layers Even with solid prompts, memory, and control flow, an agent can still go off the rails. Think of this principle as an insurance policy against the worst-case scenarios: users (or other systems) slip in instructions that hijack the agent. Prompt injection: the model blurts out PII or corporate secrets. Sensitive-data leaks: I-Content ye-Toxic noma ye-Malignant: I-hate speech, i-spam, noma impahla eyenziwe. confident but false answers. Hallucinations: : the agent “gets creative” and does something it should never do. Out-of-scope actions Akukho fix eyodwa ibandakanya konke. Ufuna : multiple safeguards that catch problems at every stage of the request/response cycle. defense-in-depth Quick Checklist is in place (jailbreak phrases, intent check). User input validation For factual tasks, answers . must reference RAG context The prompt explicitly tells the model to stick to retrieved facts. I-Output filter ibhokisi i-PII noma i-containers eyenziwe. Izimpendulo zihlanganisa i-citation / i-link ku-source. Agent and tools follow the . least privilege Critical actions route through approval or monitoring. HITL Yenza lezi zihlanganisa njengama-DevOps ezivamile: zihlanganisa, ukuhlola, futhi zihlanganisa ngokushesha. Ngakho-ke ungenza i-agent "i-autonomous" ukuguqulwa ku-responsibility ebonakalayo. Part 4 - Keep it Working Under Load Ukukhiqizwa, izixazululo zihlanganisa ngokushesha, futhi ngokuvamile ungaziwa ngokushesha, ngezinye izikhathi akuyona. Kulesi isigaba kusekelwe ukwakha isiseko se-engineering yokulawula i-agent yakho ngokuqhubekayo, ukuqinisekisa ukuthi konke kusebenza ngokushesha. . 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 I-13- Trace I-Full Execution Path : Agents will inevitably misbehave during development, updates, or even normal operation. Debugging these issues can consume countless hours trying to reproduce errors and pinpoint failures. If you’ve already implemented key principles like keeping state outside and compacting errors into context, you’re ahead. But regardless, planning for effective debugging from the start saves you serious headaches later. Problem : Uhlolokha yonke uhambo kusuka ku-user request ngokusebenzisa yonke iminyango ye-agent kanye ne-action process. I-logs ye-component ye-individual ayidingi; unemibuzo ye-end-to-end eyenza yonke imibuzo. Solution : Why this matters : Quickly identify where and why things went wrong. Debugging I-Analytics: Ukukhangisa izinzuzo kanye nezidingo zokuphucula. Ukubuyekezwa kwekhwalithi: Ukubuyekeza kanjani imiphumela embalwa emzimbeni. I-Reproductibility: Ukuguqulwa kwe-session eyodwa ngokunemba. : Maintain a full record of agent decisions and actions. Auditing Minimum data to capture : User request and parameters from prior steps. Input I-Agent State: Izinguquko eziyinhloko ngaphambi kwezinye iminyango. Umhlahlandlela: Umhlahlandlela oluphelele lithunyelwe ku-LLM (ukudluliselwa kwekhwalithi, umlando, umklamo). Imininingwane ye-LLM: Ukusabela okusheshayo ngaphambi kokusebenza. I-Tool Call: Inombolo ye-Tool ne-parameter eyenziwe. : Tool output or error. Tool result I-Agent Decision: Izinyathelo ezilandelayo noma izimpendulo ezidlulile. : Timing, model info, costs, code, and prompt versions. Metadata Use existing tracing tools where possible: LangSmith, Arize, Weights & Biases, OpenTelemetry, etc. But first, make sure you have the basics covered (see Principle 15). : Checklist Zonke izinyathelo zihlanganiswa ngokugcwele. I-logs ifakwe nge-session_id kanye ne-step_id. Interface to review full call chains. Umthamo ukuguqulwa ngokuphelele noma iyiphi i-prompt. 14 — Test Every Change : Ngaphezu kwalokho, inkonzo yakho ingatholakala ukuqala: kungenziwa, ngisho nangokuthi ngempumelelo. Kodwa kanjani uyakwazi ukuqinisekisa ukuthi kungenziwa ukusebenza emva kwe-updates? Izinguquko ku-code, ama-datasets, ama-base models, noma ama-prompts kungenziwa ngempumelelo ukuhlangabezana ne-logic eyenziwe noma ukunciphisa ukusebenza. Izindlela ezivamile ze-test zihlanganisa zonke izinzuzo ze-LLMs: Problem I-Model Drift: Ukusebenza kwandisa ngokushesha ngaphandle kwe-coding ngenxa ye-model noma i-data shifts Ukuhlobisa okusheshayo: Ukuhlobisa okusheshayo encane kungabangela ukuguqulwa kwe-output enkulu I-non-determinism: I-LLM inikeza imibuzo ahlukene kwama-input efanayo, okuvimbela izivivinyo ze-exact-match I-Errors ye-Hard-to-replicate: Ngaphandle kwe-Inputs ye-Fixed, ama-Bugs angakwazi ukucubungula. I-butterfly Effect: Izinguquko ze-cascade ezingenalutho phakathi kwezinhlelo and other LLM-specific risks Hallucinations I-Adopt a thorough, multi-layered testing strategy combining classic software tests with LLM-focused quality checks: Solution Ukuhlolwa kwama-multi-level: Ukuhlolwa kwama-unit ye-functions/prompts, ukuhlolwa kwama-integration, kanye nezinkinga ephelele ze-end-to-end Ukulinganiswa kwekhwalithi lokukhipha ye-LLM: ukuxhumana, ukuxhumana, ukucaciswa, ubuhle, nokhuseleko Ukusebenzisa i-gold datasets nge-outputs esithathwe noma izinga lokusabela okungenani lokusabela ukuhlolwa kwe-regression Ukuhlolwa okuzenzakalelayo kanye nokuxhumana ku-CI / CD pipelines Ukuxhumana abantu ukuhlolwa ebalulekile noma ephelele (umntu-in-the-loop) before deployment Iteratively test and refine prompts Ukubuyekezwa ngezinqubo ezahlukene: izingxenye, ama-prompts, amakethe / ama-agents, kanye nama-workflows ephelele : Checklist Logic kuyinto modular futhi ngokugqithiselwe ngokugqithiselwe nangokuxhumana Umgangatho we-output kuhlolwa ku-benchmark data Tests cover common cases, edge cases, failures, and malicious inputs Robustness against noisy or adversarial inputs is ensured All changes pass automated tests and are monitored in production to detect unnoticed regressions 15 - Own the Whole Stack Lezi zimo zihlanganisa yonke into, kuyinto meta-ukulawula okuyinto zihlanganisa zonke abanye. Ngaphezu kwalokho, kukhona izixhobo nezinkqubo ezininzi nezinkqubo zokuphathwa cishe noma iyiphi umsebenzi, okuyinto enhle nokushesha nokucindezeleka kwe-prototyping - kodwa futhi iyinhlangano. Ukuphaswa kakhulu kuma-framework abstraktions ikakhulukazi kuncike ukuphazamiseka, ukulawula, futhi ngezinye izikhathi, ukhuseleko. Kuyinto ebalulekile kakhulu ekuthuthukiseni ama-agent, lapho kufanele ukulawula: The inherent unpredictability of LLMs I-Logic Complex Etholakalayo Izinguquko kanye ne-self-correction Ukuphathelene nokuthuthukiswa kwekhwalithi yakho ngaphandle kokubhalisa imisebenzi eziyinhloko : 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 Izinzuzo ezininzi etholakalayo zingatholakala nge-off-the-shelf tools. Kodwa ngezinye izikhathi, ukwakha isakhiwo se-core ngokuvumelana nezinsizakalo efanayo futhi inikeza ukunambitheka, ukulawula, nokuvumelana okungcono kakhulu. Ngaphandle kwalokho, ukufaka ngokuphelele futhi rewrite yonke into ukusuka kwangaphakathi kuyinto over-engineering, futhi enhle kakhulu. Ikhoyili . As an engineer, you consciously decide when to lean on frameworks and when to take full control, fully understanding the trade-offs involved. balance : the AI tooling landscape is still evolving fast. Many current tools were built before standards solidified. They might become obsolete tomorrow — but the architectural choices you make now will stick around much longer. Remember Ukuphakama Ukwakhiwa kwe-agent ye-LLM akuyona kuphela ukulanda ama-API. Kubalulekile uklanyisa uhlelo elawula ukuhlangabezana ne-real-world messiness: ama-errors, ama-state, ama-context limits, ama-inputs amancane, kanye nezidingo ezintsha. Izinhlelo ezingu-15 etholakalayo akuyona i-theory, kukhona izifundo ze-batch-tested kusuka ku-trance. Ziza kukunceda ukuguqulwa kwe-scripts ezincinane ku-stable, i-escalable, ne-agent eyenziwe ukuthi akuyona isikhathi se-user real. Ngemuva kwalokho, kuyinto inqubo yakho, izidingo zakho, futhi yokwenza yakho. Kodwa sicela: I-LLM kuyinto enhle, kodwa kuyinto kuphela ingxenye yesistimu enhle. Isikhathi sakho njenge ingcindezi kuyinto ekhaya inqubo, ukulawula ukucindezeleka, futhi ukugcina yonke into ngokushesha. Uma ungenza into eyodwa, sicela kube lokhu: l. Because that’s the only way to go from “wow, it answered!” to “yeah, it keeps working.” slow down, build solid foundations, and plan for the long hau Faka iterating, ukuhlolwa, nokufunda. Futhi akufanele, abantu e-loop ayikho umlilo, zihlanganisa agenti yakho enhle futhi enhle. This isn’t the end. It’s just the start of building agents that actually deliver. Ukulinganiswa ukukhula ubudlelwane yakho njengomphakathi we-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