Memory Architectures for AI Agents: Short-Term, Long-Term, Episodic and Semantic Memory
A practical way to separate working context, durable facts, past experiences, and business knowledge in an agent memory design. Read article →
AI Agents
Prompt Injection in RAG: Why Retrieved Text Must Be Treated as Data
Prompt injection is not solved by a clever system prompt. Build safer RAG by separating instructions, evidence, permissions, and side effects. Read article →
Prompt Engineering vs Context Engineering for AI Agents
Prompt wording matters, but agent quality usually improves faster when the surrounding context, tools, permissions, and evidence are designed well. Read article →
Building Agent Memory with PostgreSQL: JSONB, pgvector and the Source of Truth
A practical PostgreSQL design for agent memory that combines structured facts, embeddings, metadata filters, expiry, and clear ownership. Read article →
AI Agents for Sales Research and Account Preparation
How to use AI agents for evidence-based account research, meeting briefs, CRM context, source citations, review, privacy, and measurable sales preparation. Read article →
AI Agents for Customer-Service Knowledge Search
A practical guide to building an internal customer-service knowledge agent with governed sources, permission-aware retrieval, citations, feedback, and human escalation. Read article →
AI Agents for South African Business Support Workflows
Practical considerations for South African AI-agent workflows across web and messaging channels, language, mobile access, privacy, handoffs, and operations. Read article →
What Does an AI Agent Implementation Actually Include?
A practical breakdown of the workflow, data, tools, permissions, evaluation, human handoffs, and operating work required for a production AI agent. Read article →
When Is a Chatbot Enough—and When Do You Need an AI Agent?
How to choose between a RAG chatbot, guided workflow, and tool-using AI agent based on the outcome, risk, integrations, and operating cost. Read article →
How to Choose an AI Agent Development Partner
A practical way to compare AI-agent development partners using workflow evidence, ownership, security, maintainability, operating cost, and a bounded discovery. Read article →
Least Privilege for AI Agents: Tools, Data and Tenants
Least privilege lets an agent help with a real job without giving it a master key. Start with the person, the task, and the smallest useful capability. Read article →
Human Approval Patterns for AI Agents: Review the Right Boundaries
Human approval is most useful when a person is asked to judge one clear, consequential action with the evidence and context needed to take responsibility for it. Read article →
AI Agent Kill Switches and Rollbacks: Safe Recovery
A recovery plan should let you stop one unsafe workflow, understand what already happened, and resume safely instead of shutting down everything. Read article →
Event-Driven AI Agents: Webhooks, Queues and Safe Actions
Event-driven agents become dependable when the event is verified, the work is queued, duplicate deliveries are harmless, and people can see what happens next. Read article →
RAG vs Fine-Tuning for Business Knowledge: Which Should You Use?
RAG keeps changing knowledge current, while fine-tuning changes repeatable behaviour. The right choice depends on which problem is costing you time. Read article →
AI Agent Vendor Scorecard: Compare Platforms on What Matters
A vendor scorecard helps you look past a polished agent demo and compare the things that determine whether the workflow will survive production. Read article →
Context Engineering for AI Agents: Give the Right Information
Context engineering is the everyday discipline of giving an agent the right background for one task without burying it in irrelevant or unsafe text. Read article →
Structured Outputs for AI Agents: Schemas Are Not Safety
Structured output makes an agent easier to connect to software, but tidy JSON does not make a decision true or safe. Read article →
AI Agent Threat Modeling: Trust Boundaries, Tools and Recovery
Threat modelling for an agent can start with a simple story: what can it see, what can it change, and what happens if someone gives it misleading instructions? Read article →
AI Agent Memory: What to Store, Retrieve and Forget
Agent memory should save people effort without turning guesses, stale facts, and private conversations into permanent truth. Read article →
A2UI for AI Agents: Dynamic Interfaces Without Arbitrary Code
A2UI-style interfaces let an agent ask for the right screen at the right moment while the application keeps control of rendering and permissions. Read article →
Internal AI Agents vs Customer-Facing AI Agents
How internal and customer-facing AI agents differ in data access, testing, tone, escalation, and risk—and how to choose a safe first workflow. Read article →
AI Agent Requirements Document Template
A practical template for defining users, outcomes, data, tools, permissions, evaluation, and ownership before building an AI agent. Read article →
A2A Agent Cards and Capability Discovery Explained
A practical explanation of A2A Agent Cards, discovery, capabilities, authentication, and the limits of delegation. Read article →