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AI agents are autonomous systems that plan, call tools, and execute multi-step tasks — web research, data extraction, form submission, calendar scheduling, and back-office workflow automation — without human intervention at each step.
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How to choose ai agents
AI agents are autonomous systems that plan, call tools, and execute multi-step tasks — web research, data extraction, form submission, calendar scheduling, and back-office workflow automation — without human intervention at each step. They wrap a planner, persistent memory, and tool router around a foundation model.
Buyers are operations teams and engineering teams replacing repetitive workflows. Reliability on long-horizon tasks, observability into individual decision steps, and cost per completed task — not per token — are the metrics that determine production viability.
Compare on real-task success rate outside curated demos, step-level observability and trace quality, human-in-the-loop gate controls for high-stakes actions, and total cost per outcome at your target workflow volume.
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.
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.
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.
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.
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.
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.