Guides and explainers
AI guides and explainers, researched and drawn.
How agentic AI actually works — agents and multi-agent systems, retrieval and context, MCP and model gateways, governance, security and private deployment — explained with diagrams, compared side by side, and sourced. Each explainer ends with how the Botminds platform puts the idea to work.
Agents
Agents: the explainers.
Agent harnessThe technology article →
- Agent harness vs agent frameworkWhat a framework gives you, and what a harness runs for you
- Agentic AI vs generative AIHow AI that writes becomes AI that acts, and what that adds
- AI agent vs chatbotWhat changes when the thing you chat with can act in your systems
- AI agent vs LLMWhat a language model does alone, and what turns it into an agent
Evaluation & observabilityThe technology article →
- AI agent vs LLM observabilityWhat a model-call trace shows, what an agent trace adds, and what to watch
- LLM as a judgeHow model graders work, the biases research found, and how to calibrate one
- LLM evaluation metricsBLEU to pass^k: what each metric measures and which one fits your task
- What is a golden dataset?The answer key for testing AI agents: what goes in it and how to build it
Build
Build: the explainers.
Enterprise vibe codingThe technology article →
- Vibe coding safetyWhat the research on AI-written code found, and what makes it safe
- Vibe coding security checklist29 checks across five gates, from the first prompt to production
- Vibe coding vs agentic engineeringThe same coding agents, run with or without plans, tests and review
- Vibe coding vs low codeGenerated source code or a platform’s parts: what each gives you to run
MCPThe technology article →
- A2A vs MCPGoogle’s Agent2Agent protocol and the Model Context Protocol, side by side
- MCP authenticationHow OAuth 2.1 signs people in to remote MCP servers, step by step
- MCP vs APIHow the Model Context Protocol relates to the APIs you already have
- MCP vs RAGRetrieval from an index, or a live call to a tool — and how they combine
Knowledge
Knowledge: the explainers.
Agentic RAGThe technology article →
- Agentic RAG vs RAGHow retrieval changes when an agent decides when, where and how to search
- Keyword vs semantic searchBM25, embeddings, hybrid search and re-ranking, and where each one fails
- Knowledge graph vs vector databaseRelationships against similarity: what each stores, answers and costs
- RAG vs fine-tuningWhich fixes missing knowledge, which fixes behaviour, and when to use both
AI data modelingThe technology article →
- Semantic layer vs ontologyGoverned metrics over tables against a formal model of what things are
- Structured data extraction with LLMsSchemas, structured outputs, validation layers and citations
- Structured vs unstructured dataTables, documents and the semi-structured middle — and how AI joins them
- Taxonomy vs ontologyHierarchies of terms against formal models a machine can reason over
Trust & control
Trust & control: the explainers.
AI governanceThe technology article →
- AI audit trailWhat to record for every AI decision, how to seal it, how long to keep it
- AI governance frameworkRoles, inventory, risk tiers, controls and monitoring — mapped to NIST and ISO
- AI governance vs data governanceWhat each one controls, who owns it, and where the two meet
- EU AI Act for AI agentsRisk classes, provider and deployer duties, and the dates that apply
- SR 11-7 and generative AIModel risk guidance, its 2026 replacement, and how banks govern LLMs and agents
Topic guides
Long-form guides across industries.
AI contract reviewHow AI contract review works, how contract review software differs from CLM, which contracts to start with in each industry, and what to demand on accuracy.AI in bankingHow AI in banking works today: AI agents for lending, KYC and AML, operations and wealth, what regulators expect, and how to choose and pilot a vendor.AI underwritingAI in insurance underwriting, explained: submission intake, appetite triage, personal, commercial, specialty and life lines, MGA oversight and fair testing.AML softwareWhat anti-money laundering software does, where AI helps with KYC, sanctions screening, alert reviews and SAR narratives, and what it should never decide.Insurance claims automationHow automated insurance claims processing works: FNOL triage, coverage review, injury, fraud and subrogation, what stays human, and how to pick claims software.Intelligent document processingWhat intelligent document processing is, how agentic IDP differs from OCR and templates, IDP use cases by industry, how to measure accuracy and what to ask.What is agentic AI?What agentic AI is, what an AI agent is, how agentic AI differs from RPA and workflow automation, where people stay in the loop, and examples by industry.
Buyer’s guides
Buyer’s guides by industry.
Healthcare & Life SciencesHow to choose AI for pharma regulatory, quality, clinical and medical work: where it pays off first, what to demand on citations and sign-off, and how to pilot.LendingHow to choose AI for lending: where AI loan processing and credit underwriting software pay off, what to demand on accuracy and sign-off, and how to pilot.InsuranceHow insurers, MGAs and brokers choose AI for claims and underwriting automation: where to start, what to demand, how to pilot, and the rules that apply.UtilitiesHow electricity and water utilities choose AI: where it pays off first, what to demand on accuracy and sign-off, how to run a pilot, and what waiting costs.AirlinesAI for airlines, explained for buyers: where agentic AI pays off in flight operations, maintenance, crew, revenue, disruption, cargo and compliance, what to demand on citations and sign-off, how to run a pilot, and what waiting costs.Investment ManagementAI in asset management for buyers: where agentic AI pays off from research to client reporting, what to demand on citations and sign-off, and how to pilot it.EducationAI in education for buyers: where agentic AI pays off first on campus and in K-12, what to demand on citations and sign-off, and how to pilot it.BankingAI use cases in banking operations, area by area: KYC, BSA/AML, sanctions, fraud, Reg E disputes, payments, deposits, treasury management, trade finance, compliance and reporting, with every value cited and a named person deciding.Cross-IndustryHow to choose AI for back-office work: where AI for finance, legal, procurement, audit and HR pays off, what to demand on citations and sign-off, how to pilot.
Read about it.
Then see it run.
Bring one process and its documents. We’ll show you the agents, the controls and the record, running on your data.