Large language models (LLMs) can hold conversations, draft documents, and assist with code. Yet they are fundamentally reactive: they respond to prompts, they do not plan across sessions, commit actions, or coordinate with other systems unless you wrap them in the right architecture. That wrapper is agentic AI—and when you need scale, specialization, and fault tolerance, you move to multi-agent AI.

From chat to agents

An agent couples an LLM (or other model) with memory, tools, and a control loop: observe, plan, act, reflect. Multi-agent systems distribute work across agents with different roles—researcher, executor, critic, router—connected by explicit protocols instead of one overloaded prompt.

Enterprises adopt this pattern when a single chain-of-thought trace is not enough: workflows cross APIs, humans, and long horizons; failures must localize rather than take down the whole assistant.

Why pure LLMs fall short

• No durable initiative — Models wait for the next user turn; they do not autonomously advance a ticket, open a PR, or reconcile a ledger without orchestration.
• Shallow planning — Strong at local coherence, weak at guaranteed multi-step execution unless you externalize plans and verify each step.
• Session-bound memory — Long-term facts need vector stores, graph edges, or canonical records—not only context windows.
• Limited tool use without scaffolding — Calling HTTP, SQL, or sandboxes safely requires allowlists, schemas, retries, and audit logs.
• Error handling — Models rarely self-correct unless you add evaluation agents, unit checks, or human escalation paths.

Agentic workflow (single agent)

A practical loop looks like:

1. Perceive — Ingest user goal, policies, and environment signals (tickets, metrics, docs).
2. Plan — Decompose into steps with explicit success criteria; prefer structured outputs (JSON) over prose-only plans.
3. Act — Invoke tools with timeouts, idempotency keys, and least-privilege credentials.
4. Verify — Run automated checks; route failures to repair or escalation.
5. Remember — Persist decisions and artifacts for compliance and future runs.

Multi-agent AI: when one brain is not enough

Multi-agent setups assign roles: for example, a planner produces milestones, a critic probes edge cases, and a specialist calls domain APIs. Benefits include:

• Parallelism — Independent subtasks progress concurrently with clear handoffs.
• Resilience — One agent failing does not erase global state if messages and checkpoints are shared safely.
• Specialization — Smaller prompts per role often beat one giant system prompt.
• Governance — Easier to trace which component approved a sensitive action.

Components you typically need: message bus or queue, shared state (with versioning), schema for inter-agent payloads, human-in-the-loop gates for irreversible operations, and evaluation harnesses so changes do not regress behavior silently.

Challenges

Coordination complexity grows quickly—negotiation deadlocks, duplicate work, and oscillating policies are common if you lack clear termination rules. Non-stationarity means other agents change behavior while you learn; classical single-agent RL assumptions break. Security matters: treat agents as privileged users; isolate tools; red-team prompt injection and tool abuse through indirect inputs in retrieved documents.

Ethically, accountability and bias compound when outputs are emergent from many model calls—keep humans accountable for deployments, log prompts and outputs where policy allows, and test for demographic skew in screening or routing agents.

Engineering checklist

• Define tool contracts (OpenAPI-style) and validate arguments before execution.
• Use structured logging and correlation IDs across agents.
• Add rate limits, cost ceilings, and circuit breakers around model and API calls.
• Version prompts and policies; treat them like config, not invisible code.
• Ship offline evals plus shadow mode before promoting autonomous branches.

Conclusion

Multi-agent AI is not hype—it is a systems pattern for making LLMs reliable in production: specialized roles, explicit interfaces, and operational discipline. Start with a single well-instrumented agent, then split responsibilities when latency, quality, or ownership boundaries demand it. The organizations that win will pair frontier models with boring, excellent engineering: testing, observability, and human oversight where stakes are high.

Read on Don C John’s site · 3 min read

E-commerce platforms today compete on speed, accuracy, and resilience. Customers expect inventory to match reality, prices to update without glitches, and checkout to survive flash traffic. Monoliths can still work early on—but teams that outgrow a single deploy unit usually shift toward bounded contexts and event-driven integration.

Why inventory is the hardest problem

Inventory touches catalog, warehouses, carts, payments, and fraud. A synchronous “reserve stock” call that blocks checkout will fail under load. A better approach combines:

• Optimistic UX with server-side reconciliation.
• Idempotent reservation tokens so retries do not double-charge stock.
• Outbox pattern or log-based pipelines so database commits and message publishes stay consistent.

Microservices without micro-madness

“Microservices” is not a goal—independent scaling and team ownership are. Common slices:

• Catalog — Search, merchandising rules, SEO surfaces.
• Cart & checkout — Sessions, taxes, promotions (often its own complexity).
• Orders & fulfillment — State machines, partial shipments, returns.
• Payments — PCI scope minimization, webhooks, reconciliation jobs.

Between services, prefer contracts (schemas, versioning) and observability (distributed tracing) over ad-hoc JSON.

Frontend: React and the edge of truth

React (and frameworks like Next.js) excel at streaming UIs, component-driven design, and A/B tests. Pair them with a Backend-for-Frontend (BFF) when the browser should not orchestrate five microservices directly—aggregate on the server, cache intelligently, and hide churn.

For real-time stock or live order tracking, WebSockets or SSE can complement REST; fall back to polling with backoff when connections are flaky.

NestJS and “boring” Node backends

NestJS gives structure (modules, DI, pipes) that helps large teams keep validation and authorization consistent. Pair it with a message broker (Redis Streams, RabbitMQ, Kafka—pick for your scale) when workflows cross services. Fastify under the same organizational patterns can win on throughput where it matters.

Data and caching

PostgreSQL remains the workhorse for orders and money-adjacent tables. Use Redis for hot keys: session carts, rate limits, and short-lived locks. Avoid caching without TTL and invalidation stories—stale inventory erodes trust faster than a slow page.

Security and compliance

Webhooks from payments need signature verification; never trust raw bodies. PII minimization, retention policies, and regional data residency should be designed in—not bolted on after a breach headline.

Conclusion

Modern e-commerce is distributed systems engineering with a storefront. Invest early in inventory correctness, idempotency, and clear service boundaries; use React and Node ecosystems to ship quickly without sacrificing operational maturity.

Read on Don C John’s site · 2 min read

Customer expectations moved from “email us in 24 hours” to instant, accurate answers across chat, voice, and social. Building that with NLP, managed AI (e.g., Vertex AI), and classic software hygiene is very doable—if you respect limits of raw generative models.

Start with intents, not vibes

Before LLMs, teams built intent classifiers and slot filling; that discipline still matters. Define:

• Top intents — order status, refund policy, password reset, feature questions.
• Grounding sources — help center articles, internal runbooks, policy PDFs with freshness dates.
• Escalation rules — when to hand off to human agents with full transcript and metadata.

LLMs can route and draft, but policies and tool results should drive factual answers.

Architecture sketch

1. Ingress — Chat widget, WhatsApp, phone ASR—normalize to text and locale.
2. Orchestrator — Selects retrieval vs. FAQ vs. ticket creation; enforces auth for account-specific queries.
3. Retrieval — Hybrid search (keywords + embeddings) over approved corpora; attach citations in agent responses.
4. Generation — Template + model: answer in brand voice, refuse when evidence is weak.
5. Feedback loop — Thumbs, resolution tags, and conversation mining to fix gaps.

Python, TensorFlow, and when you still train

Many support flows run on API-first models (speech, translation, classification). Custom TensorFlow or PyTorch models shine when you have proprietary corpora, strict latency SLAs, or offline constraints. Vertex AI can host both pretrained APIs and your containers—pick cost vs. control deliberately.

Safety and tone

• PII redaction before logs and analytics.
• Jailbreak resistance—never expose raw system prompts; validate tool arguments.
• Brand guardrails—length limits, empathy cues, and prohibited claims lists.

Measure containment (resolved without human), CSAT, and average handle time—not just model perplexity.

Conclusion

Great customer care AI combines retrieval, deterministic workflows, and careful prompting. Ship small vertical slices (e.g., “where is my order?”), instrument everything, then expand. The moat is your knowledge ops—not the model weights alone.

Read on Don C John’s site · 2 min read

Generative AI can draft job descriptions, screen resumes, and suggest interview questions. Used thoughtfully, it reduces busywork; used carelessly, it amplifies bias and compliance risk. The goal is decision support for humans, not unreviewed automation of hiring outcomes.

What LLMs do well

• Normalizing CVs into structured summaries (skills, industries, tenure bands).
• Generating interview guides aligned to a rubric.
• Matching explanations—why a profile overlaps a role’s requirements—when fed explicit criteria.
• Drafting outreach with inclusive language checks.

They are weaker at fair cross-company comparability without calibrated rubrics and legal defensibility without audit logs.

Design patterns

1. Structured extraction — Force JSON/schema output; validate against enums you control.
2. Dual review — Model proposes shortlists; humans confirm; log both.
3. Blind stages where feasible — strip names/photos until later rounds if policy allows.
4. Periodic bias audits — Compare pass rates across groups; retrain or adjust prompts when drift appears.

Retrieval over résumé stuffing

Candidates optimize for keywords; semantic search across embeddings helps—but ground explanations in quoted snippets to avoid hallucinated qualifications.

Regulatory posture

Jurisdictions differ on automated employment decisions. Default to transparency (candidates informed where required), human review for adverse actions, and data minimization—retain only what your policy allows.

Conclusion

AI in recruitment works best as human-in-the-loop software with schemas, logging, and metrics tuned to fairness—not as a black-box scorer. Invest in rubrics and governance alongside model choice.

Read on Don C John’s site · 2 min read

Product reviews are noisy: enthusiasm, sarcasm, multilingual text, and spam sit side by side. A practical review analyser turns that mess into themes, sentiment, and risk flags product teams can act on—often with a modest TensorFlow model plus solid preprocessing.

Problem framing

Decide what “success” means:

• Star prediction vs. aspect sentiment (battery life, shipping, support).
• Toxicity / abuse detection for moderation queues.
• Topic clustering to surface recurring defects.

Most teams ship multi-headed models or separate lightweight classifiers per task rather than one giant end-to-end beast.

Pipeline

1. Ingest & normalize — Strip HTML, deduplicate, language ID, PII scrubbing if needed.
2. Tokenization — Subword models handle out-of-vocabulary brand names well.
3. Model — Start with a strong baseline (e.g., fine-tuned transformer export to TF SavedModel, or CNN+LSTM for tight CPU budgets).
4. Calibration — Platt scaling or temperature scaling so probability thresholds mean something in dashboards.
5. Explainability — LIME/SHAP snippets or attention heatmaps for analyst trust—not for end customers unless vetted.

Evaluation

Balance precision/recall for toxicity; use macro-F1 on rare classes. Watch slice metrics (languages, short reviews) to catch silent failures. Backtest on historical spikes (recalls, app updates) before trusting automated alerts.

Serving

Export to TensorFlow Serving, ONNX, or serverless containers depending on volume. Batch nightly retraining when labels accumulate; stream scoring for near-real-time moderation if policy demands.

Conclusion

Review analysers win on data quality and evaluation discipline, not exotic architectures. Ship a thin vertical slice—say, 3-star-and-below routing with reasons—then layer complexity as product owners trust the system.

Read on Don C John’s site · 2 min read

Early-career engineers need structure, feedback, and safe practice. A modern digital learning platform combines curated curricula with automated grading, code runners, and—where appropriate—AI tutors that explain mistakes without doing the work for learners.

Platform pillars

• Competency maps — Skills decomposed into observable objectives (not just video watch time).
• Hands-on labs — Sandboxed environments; deterministic test suites for autograding.
• Mentor visibility — Coaches see attempt histories, not only final scores.
• Accessibility — Captions, keyboard flows, and low-bandwidth modes.

React on the learner surface

React (or Next.js) delivers responsive modules, progressive disclosure, and interactive quizzes. Pair with SSR or static generation for marketing and syllabus pages; use client components for editors and simulations.

NestJS (or similar) for the learning API

Auth, cohorts, attempt storage, and webhook integrations (HRIS, SSO) belong in a typed backend. Keep grading async: queue submissions, scale workers, prevent duplicate points on retries.

AI as copilot, not oracle

Use models to:

• Generate hints after a failed test (grounded in the spec).
• Summarize rubric feedback mentors already provided.
• Propose reading paths from weak areas in analytics.

Avoid fully automated pass/fail on subjective work without human review.

Measuring success

Track time-to-first-pass, retention week-over-week, and on-job performance proxies if HR partners can share them—optimize for outcomes, not vanity completion rates.

Conclusion

Digital learning platforms succeed when pedagogy leads and engineering reduces friction—fast feedback loops, honest assessments, and integrations that meet enterprises where they operate.

Read on Don C John’s site · 2 min read

Engineering teams repeatedly build the same “ML glue”: summarize a URL, ask questions of a PDF, spin up a mock API for frontend work, or validate an experiment config. Packaging these as internal tools shortens feedback loops—if you keep them maintainable.

Principles

• Opinionated defaults — One happy path; advanced tabs optional.
• Tenant safety — AuthN/Z, quotas, and content scanning for uploads.
• Observability — Trace ID from UI → worker → model host.
• Cost caps — Per-team tokens and GPU minutes.

Web summarization

Fetch → readability extraction → chunk → rank → abstractive or extractive summary. Add caching keyed by URL hash and robots.txt respect. Surface sources so developers trust the output.

PDF and document Q&A

Parse with layout-aware extractors; embed chunks; answer with citation-required prompting. For regulated docs, keep page-level provenance and retention policies explicit.

Mock APIs

Generate OpenAPI from examples or natural language sketches; host ephemeral mocks with record/replay to stabilize tests. Pair with contract tests against the real service as it matures.

Stack notes

TensorFlow / Keras remain strong for custom models you fully control and export (TF Lite, TF Serving). Next.js gives you auth routes, server actions, and streaming UI for long operations—ideal for developer portals wrapping these capabilities.

Guardrails

Red-team for indirect prompt injection in fetched content; sandbox code execution if users supply snippets; never trust client-provided URLs without SSRF controls.

Conclusion

ML dev toolkits pay off when they are boring to operate: clear SLAs, backups of configs, and product owners who treat them as first-class internal software—not demos glued to a README.

Read on Don C John’s site · 2 min read