Index // Collection

Topics

Browse every Replyant topic — AI agents, automation, evals, governance, MCP, and ROI — across business-focused Blog posts and technical Lab research.

Replyant writes about one subject from two angles: how AI agents and automation earn their keep inside a real business, and how to engineer them so they keep working after launch.

The Blog is written for operators and executives. It covers AI strategy, ROI measurement, governance, compliance, pricing, and the hire-versus-automate decisions that land on a leadership team’s desk.

The Lab is written for engineers. It covers agent architecture, tool calling, the Model Context Protocol, retrieval, evals, observability, and the agent security research behind our work on prompt injection and tool poisoning.

Every topic below links to the posts tagged with it, from both sections. Pick the one closest to the decision or system you are working on, or start with AI Agents for the broadest view.

AI Agent & Chatbot Pricing Guide

AI agent and chatbot pricing — what custom solutions actually cost, where the money goes, and how to avoid overspending.

AI Agent Architecture & System Design

Technical deep dives into AI agent architecture — orchestration patterns, system design, and the decisions that shape production-grade agents.

AI Agent Governance: Control & Compliance

Governing AI agents — access control, permissions, audit trails, and compliance as agents gain autonomy and act on real systems.

AI Agent Integration: APIs, Databases & Protocols

How to connect AI agents to existing business systems — APIs, databases, protocols, and the integration patterns that work in production.

AI Agent ROI: Measurement & Optimization

Measuring and maximizing AI agent ROI — practical frameworks, real benchmark numbers, and the metrics that matter to your CFO and leadership.

AI Agent Tool Calling: Protocols & Patterns

How AI agent tool calling works — from protocol design to execution sandboxing and the patterns that make tool use reliable.

AI Agents in Logistics: Shipper Operations

AI agents for shipper logistics: freight invoice audit, exception management, tender failures, and detention disputes, measured in dollars recovered.

AI Agents: Building & Deploying Intelligent Automation

Articles on building, deploying, and scaling AI agents that deliver real business value — from architecture and evals to production ROI.

AI Business Strategy & Decision Frameworks

AI strategy for business leaders — where to invest, how to measure ROI, and the decision frameworks that separate winners from the rest.

AI Developer Tools & Engineering Workflows

AI-powered developer tools and workflows — coding agents, CI/CD integration, agent security, and the productivity engineering that holds up.

AI Readiness: Assessment & Prerequisites

Is your business ready for AI? Assessment frameworks, prerequisites, and the organizational foundations that determine AI success.

AI Strategy: Where Agents Fit in the Business

Executive AI strategy for agents — build vs buy, sequencing pilots to production, and aligning AI investment with business outcomes.

Business Automation with AI Agents

Business automation with AI agents — identifying high-ROI opportunities, implementation strategies, and avoiding the common pitfalls.

Claude Code: Production Workflows & Extensions

Practical guides to using Claude Code in production — hooks, MCP servers, plugins, custom skills, and real-world dev workflows.

Compliance

AI compliance and regulatory readiness — navigating the EU AI Act, risk classification, and governance frameworks for business AI deployments.

Evals: Testing AI Agent Quality

Evaluating AI agents — eval suites, CI regression testing, and measuring agent accuracy and cost before and after you ship.

Hiring vs. Automating: When to Add AI Instead

The hire vs. automate decision — when to add headcount, when to deploy AI agents, and how to get the balance between people and agents right.

LLMs: How Large Language Models Power AI Agents

How large language models power AI agents — capabilities, limitations, and the technical foundations behind modern agent systems.

MCP: Model Context Protocol for AI Agents

How the Model Context Protocol (MCP) connects AI agents to business systems — protocol design, server implementation, and production deployment.

Multi-Agent Systems: Orchestration & Coordination

How multi-agent architectures work in practice — orchestration patterns, coordination strategies, and real-world cost tradeoffs.

Observability: Watching AI Agents in Production

Observing AI agents in production — tracing, logging, cost and latency monitoring, and catching drift before it reaches users.

Process Design for AI Automation

Designing business processes for AI automation — workflow mapping, handoff points, and building processes that agents can actually execute.

Prompt Engineering for Production AI Agents

System prompt design for AI agents — instruction architecture, safety constraints, and the patterns that make agents reliable in production.

Retrieval-Augmented Generation (RAG) for AI Agents

Practical RAG engineering for production agents — agentic retrieval, query routing, verify loops, and the patterns that scale past naive pipelines.

Workflows: Automating Business Process with Agents

Automating business workflows with AI agents — process design, orchestration, and integrating multi-step automation into operations.