4.8/5 ★★★★★Topic
Guides on agentic AI — systems where AI agents plan, decide, and act across multi-step workflows in production business environments.
10 posts
Agentic AI is AI that acts — it plans tasks, uses tools, makes decisions, and completes multi-step work without waiting for a human prompt at each step. A chatbot answers questions. An AI agent handles the entire workflow. This guide explains the difference and what it means for business operations.
Building a production AI agent requires five things before writing a line of code: a clearly scoped task, accessible data, defined success criteria, a human escalation pathway, and a monitoring plan. This checklist covers what enterprises actually need to get right.
Every enterprise AI agent project that fails in 2026 fails for the same reason: the five prerequisites weren't checked. Here's the checklist — process documentation, historical data, error definitions, ownership, and infrastructure — plus a realistic build timeline and ROI calculation.
Before hiring an AI agent development company, ask seven questions that separate teams with production experience from teams that have only built demos. This audit covers architecture, deployment, monitoring, failure handling, and the cost signals that reveal real capability.
RPA automates clicks. Agentic workflows automate decisions. We stopped recommending UiPath when the exception rates on real processes made click-replay automation a liability. Here's when each approach fits and how to decide.
Both Anthropic and OpenAI ship agent SDKs in 2026. Claude excels at long-context reasoning and structured output. OpenAI leads in ecosystem breadth and multi-agent handoffs. A technical comparison for production teams.
Agentic AI in enterprise means autonomous agents executing procurement, compliance, and operations workflows — not chatbots. Here's what production agentic AI looks like, what fails, and how to build the business case.
Standard RAG retrieves and generates in one pass. Agentic RAG plans, retrieves iteratively, verifies, and acts. Here's when each architecture is the right choice — and what production pitfalls to watch for.
Agentic AI handles exception-heavy, judgment-intensive processes that break traditional automation. Traditional RPA and workflow automation remain the right choice for deterministic, rule-bound processes. Here is the decision framework.
An AI agent is software that completes multi-step tasks autonomously — using tools, making decisions, and taking actions without a human at each step. Here is what that means in practice, with three real production examples.
