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AUTHOR / BHAVIN-MISTRY
Bhavin Mistry
AI strategy and engineering leader based in Melbourne, Australia. Bhavin takes generative AI, agentic systems and retrieval architectures from exploratory prototypes to reliable, observable production systems in regulated industries such as financial services, with a focus on enterprise RAG, agent failure modes, AI governance (APRA, SOC 2) and LLMOps. He holds a Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications from The University of Texas at Austin, and is the founder of Learn AI Engineering.
- Role
- AI strategy and engineering leader; founder of Learn AI Engineering
- Based in
- Melbourne, Australia
- Education
- Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications, The University of Texas at Austin
Enterprise RAGAgentic AIAI GovernanceLLMOpsForward Deployed Engineering
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- External facts are cited inline, and illustrative scenarios are labelled as illustrative.
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38 articles; most recently reviewed Oct 14, 2026.
Articles
38 articlesFDE· Intermediate
Build an AI Business Case with a Skill That Won't Invent Numbers
A free AI skill that builds a funding-ready business case with options, an assumptions register and best, base and worst cases, and refuses to make up numbers.
Oct 14, 2026 10mRead
FDE· Foundations
Practise Forward Deployed Engineer Interviews with an AI Coach Skill
A free AI skill that runs timed FDE drills, client role-plays and deployment-design rounds and scores you against a rubric. Includes a real coaching session.
Oct 14, 2026 9mRead
AI Agents· Intermediate
Review an AI Agent's Approval Policy in 10 Minutes with an AI Skill
Paste your agent's tool list into a free AI skill and get a risk-tiered approval review, ranked findings and tests, with a real example run and install steps.
Oct 14, 2026 9mRead
Production AI· Intermediate
AI Strategy for Engineering Leaders: Choose, Fund and Stop
How engineering leaders decide which AI projects to back: a portfolio view, stage gates, stop criteria and evidence-based reporting, with reusable scorecards.
Oct 11, 2026 9mRead
Production AI· Intermediate
An AI Governance Framework for Engineering Teams
A lightweight AI governance framework for engineering teams: inventory, risk tiers, approvals, evaluation gates, incidents and a review cadence.
Oct 11, 2026 11mRead
Production AI· Intermediate
API or Local Models: Where the Cost Break-Even Sits
When does running a model yourself beat paying for an API? A total-cost framework, an illustrative break-even calculation and the non-cost factors that decide.
Oct 11, 2026 9mRead
Architecture· Foundations
Context Engineering vs Prompt Engineering: What Changes
What context engineering is and how it differs from prompt engineering: what goes into context, budgets, ordering, compaction and how to evaluate it.
Oct 11, 2026 9mRead
RAG· Foundations
Embeddings and Vector Search, Explained for Engineers
How embeddings and vector search work: similarity, nearest-neighbour indexes, filters, hybrid search and the version trap, explained step by step for engineers.
Oct 11, 2026 10mRead
FDE· Intermediate
How to Build the Business Case for an AI Project
How to build a credible AI business case: costs, value, adoption ramp and best, base and worst cases, with a clearly illustrative worked example.
Oct 11, 2026 10mRead
Evaluation· Advanced
How to Evaluate AI Agents: Beyond Final-Answer Accuracy
How to evaluate AI agents that take many steps: outcome, trajectory, tool use, policy adherence and cost, with a task-based golden set and regression checks.
Oct 11, 2026 10mRead
Production AI· Intermediate
How to Measure the Impact of AI Coding Tools on Your Team
How to tell whether AI coding tools are working: measure delivery, quality and developer experience against a baseline, and avoid activity metrics that mislead.
Oct 11, 2026 10mRead
Architecture· Advanced
LLM Gateways: What They Do and How to Choose One
What an LLM gateway does, when you need one and how to choose: authentication, routing, budgets, caching, logging and policy, with a vendor-neutral scorecard.
Oct 11, 2026 9mRead
Production AI· Intermediate
LLM Observability: What to Trace and Why
What to trace and log in a production LLM app: prompts, retrieved context, tool calls, tokens, cost and latency, with sampling, privacy and alerting guidance.
Oct 11, 2026 9mRead
AI Agents· Intermediate
Prompt Injection, Explained: Attacks and Defences That Hold
How prompt injection works and which defences hold: direct and indirect attacks, why filters fail, least privilege, approvals and red-team tests.
Oct 11, 2026 10mRead
Architecture· Foundations
Prompting, RAG or Fine-Tuning: A Decision Guide
Which technique first? A decision guide for prompting, RAG and fine-tuning: what each changes, what it costs to maintain and how to decide with an evaluation.
Oct 11, 2026 10mRead
FDE· Foundations
Scoring AI Use Cases Before You Build Anything
How to choose a first AI use case: score value, data readiness, feasibility, risk and sponsor on one page, then test it with a short proof of concept.
Oct 11, 2026 9mRead
Architecture· Intermediate
What an AI Platform Engineer Actually Owns
What an AI platform team builds and runs: gateway, serving, evaluation pipelines, observability, cost and security, and where product teams take over.
Oct 11, 2026 9mRead
AI Agents· Intermediate
When Should an AI Agent Ask Permission? Human-in-the-Loop Approval Design
When should an autonomous agent stop and ask? A risk-tiered approval framework drawn from Muse, Dots and Grok Bot, with policy-as-code you can reuse.
Oct 7, 2026 10mRead
AI Agents· Advanced
AI Agent Identity: Why Every Agent Needs Its Own Credentials (Not Your Password)
Always-on agents need their own identities and scoped credentials, not your password. How Dots, Muse and Grok Bot handle secrets, and a pattern to copy.
Oct 7, 2026 10mRead
AI Agents· Foundations
Dots vs Muse vs Grok Bot vs Gemini Spark: Best Always-On AI Agent in 2026
OpenAI Dots, Meta Muse, xAI Grok Bot and Google Gemini Spark compared on runtime, security, approvals, integrations and price, with a pick for every use case.
Oct 7, 2026 11mRead
AI Agents· Intermediate
How to Build Your Own 24/7 Personal AI Agent in 2026 (Hosted or DIY)
Build a personal AI agent that runs 24/7: choose hosted (Dots, Muse, Grok Bot) or DIY, then wire up scheduling, memory, MCP tools, approvals and an audit log.
Oct 7, 2026 13mRead
AI Agents· Advanced
How to Deploy AI Agents in the Enterprise: A 2026 Governance Playbook
How enterprises roll out always-on AI agents safely: first use cases, an identity per agent, approval tiers, SIEM audit logs, evals and a 90-day pilot plan.
Oct 7, 2026 12mRead
FDE· Intermediate
Forward Deployed Engineer in 2026: Skills, Role and Why Always-On Agents Need FDEs
What a Forward Deployed Engineer does in 2026, the FDE skill stack, why always-on agents like Dots and Grok Bot need FDEs, and how to train your team.
Oct 7, 2026 10mRead
AI Agents· Foundations
What Is Grok Bot? xAI's Always-On AI Teammates Explained (Pricing, Routines, Risks)
Grok Bot gives xAI's agents a cloud computer, skills learned from screen recordings and scheduled routines. How it works, what it costs, and the shared-VM risk.
Oct 7, 2026 9mRead
AI Agents· Intermediate
Meta Muse AI Agent Explained: How It Works, Pricing and the Sentinel Safety Model
Meta's Muse runs in a per-user Secure VM while a separate Sentinel agent approves every action. How it works, pricing, and how to copy the guard-agent pattern.
Oct 7, 2026 10mRead
AI Agents· Intermediate
OpenAI DevDay 2026 for Agent Builders: Agents API Computer Use, GPT-6.1 Sol, MCP Events
The OpenAI DevDay 2026 announcements that matter for agent builders: Agents API computer use, GPT-6.1 Sol at one-fifth the price, MCP Events and Dots.
Oct 7, 2026 9mRead
AI Agents· Foundations
What Are OpenAI Dots? Always-On ChatGPT Agents Explained (2026)
OpenAI Dots are always-on ChatGPT agents with their own cloud computer. How they work, pricing, approval rules, specialist Dots, and how to set one up.
Oct 7, 2026 9mRead
Production AI· Intermediate
The 90-Day Enterprise AI Adoption Playbook
A practical 90-day plan for enterprise AI adoption: foundation, controlled scale and production, plus a 5-level maturity model to show where you stand.
Sep 11, 2026 15mRead
Production AI· Intermediate
Inside the AI-Augmented Developer Workflow
One ticket, start to finish, through an AI-augmented development workflow: where the agent helps, where it stalls and where a human has to step in.
Sep 11, 2026 13mRead
Production AI· Advanced
The AI-Native SDLC: What AI Does and What Humans Decide
Every stage of the software lifecycle rebuilt around AI: what AI now does, what changes and what stays human-controlled at each of twelve stages.
Sep 11, 2026 15mRead
Architecture· Advanced
The Enterprise AI Engineering Blueprint
A reference architecture for enterprise AI: gateway, orchestration, RAG, MCP tools, identity, guardrails, evaluation, observability and governance.
Sep 11, 2026 16mRead
Production AI· Advanced
Enterprise AI Guardrails: A Ten-Layer Defense in Depth
A ten-layer guardrail model for enterprise AI: prompt injection, least-privilege tools, approved models, audit trails, kill switches and MCP security.
Sep 11, 2026 17mRead
AI Agents· Advanced
AI Agent Loops: Stopping Conditions and Guardrails for Production
Why agents loop forever and the guardrails that stop them: iteration limits, cost budgets, tool-call deduplication, progress checks and clear 'done' criteria.
Sep 4, 2026 12mRead
Production AI· Intermediate
How to Reduce LLM Costs by 90% Without Hurting Quality
Semantic caching, model routing and context compression compound. How each lever works, how to apply it safely, the maths behind 90% and where teams go wrong.
Sep 4, 2026 11mRead
Evaluation· Advanced
LLM Evaluation Fundamentals for Production Systems
How to evaluate a production LLM system: golden sets from real traffic, retrieval and generation metrics, calibrated LLM judges, CI gates and online monitoring.
Sep 4, 2026 12mRead
RAG· Advanced
Production RAG Architecture: A Complete Guide
The full production RAG architecture: ingestion, chunking, embeddings, hybrid search, reranking, generation, evaluation and observability, with code.
Sep 4, 2026 14mRead
MCP· Intermediate
What is MCP? Model Context Protocol Explained for AI Engineers
MCP is the open standard that lets any AI agent use any tool server. How it works, its three primitives, transports, security and when plain function calls win.
Sep 4, 2026 11mRead
RAG· Intermediate
Why Good Retrieval Still Produces Bad Answers
Debug RAG failures with a runnable Python exercise that separates retrieval misses, dropped evidence, unsupported claims and incorrect refusals.
Sep 4, 2026 8mRead