Question 1

What is an AI agent?

Accepted Answer

An AI agent is an autonomous system that plans multi-step tasks, calls external tools, and executes workflows on behalf of users without requiring human input at each step. It wraps a planner, persistent memory, and tool router around a foundation model so the model can take real-world actions rather than only generate text in a chat interface.

Question 2

How much do AI agent platforms cost?

Accepted Answer

Hobbyist plans run £15–35 per month with strict run caps. Operations-scale deployments land between £150–600 per month. Enterprise plans with observability, governance controls, and SLA-backed orchestration reach £1500–8000 per month and scale with run volume and tool-call frequency.

Question 3

AI agents versus traditional automation — what is the difference?

Accepted Answer

Traditional automation excels at deterministic, rule-based tasks where every step is defined in advance. Agents handle ambiguity, unstructured input, and tasks that require in-flight reasoning and decision-making. Use deterministic automation where it already works; deploy agents only where genuine flexibility pays for the added cost and reliability risk.

Question 4

What are the best use cases for AI agents?

Accepted Answer

Strong fits include multi-source research synthesis, browser-driven data extraction, structured ticket triage with ambiguous inputs, and pre-meeting preparation from calendar and CRM context. Weak fits include high-stakes irreversible actions and tasks where deterministic automation already works reliably — agents add cost and risk without proportional benefit there.

Question 5

How reliable are AI agents in production?

Accepted Answer

Reliability varies sharply by task complexity, workflow length, and platform quality. Curated demos reach high success rates; real production deployments on long-horizon tasks with edge cases typically land between 60–80 percent task completion. Observability, deterministic fallback paths, and human-in-the-loop gates for critical actions are essential for production viability.

Question 6

What observability do I need for a production AI agent?

Accepted Answer

You need step-level execution traces showing every tool call, the inputs and outputs, decision rationales, and replayable run histories. Aggregate success-rate dashboards alone are insufficient — debugging production failures requires the full decision trace. Treat any platform that cannot provide this as unsuitable for production workflows.

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