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AI Agent Decision Calculator

Decide when to build an AI Agent — and when simpler automation is smarter.

1. Score your use case

For each dimension, choose how complex or demanding the use case is (1 = simple, 4 = advanced).

How complex are the inputs the system must understand?
How complex are the decisions needed to move work forward?
How many tools/APIs are involved and how variable are the actions?
How autonomous do you want this system to be?
How important is it that behaviour improves over time?
How broad is the knowledge the system needs to work with?

2. Cost & risk overlay

Use these to sanity-check if a full AI Agent is practical right now.

How sensitive is this use case to ongoing automation/AI costs?
What is the impact if things go wrong?
How important are empathy and human judgement?
How ready are your systems, integrations and data?
Result
Awaiting input…
Score your use case on the left, then click “Calculate recommendation”.
The calculator will suggest deterministic automation, an LLM assistant, or a full AI Agent — and highlight any cost/risk caveats.
Average capability: –
Overlay: –
Caveats to consider:
    This tool is a guide, not a rule. iAhub can run a deeper assessment workshop to confirm the right design, architecture, and rollout plan for your organisation.
    AI Agent Decision Calculator

    Scoring

    1. Score your use case

    For each dimension, choose how complex or demanding the use case is (1 = simple, 4 = advanced).
    How complex are the inputs the system must understand?
    How complex are the decisions needed to move work forward?
    How many tools/APIs are involved and how variable are the actions?
    How autonomous do you want this system to be?
    How important is it that behaviour improves over time?
    How broad is the knowledge the system needs to work with?

    2. Cost & risk overlay

    Use these to sanity-check if a full AI Agent is practical right now.

    How sensitive is this use case to ongoing automation/AI costs?
    What is the impact if things go wrong?
    How important are empathy and human judgement?
    How ready are your systems, integrations and data?

    Results

    Results

    Awaiting input…

    Score your use case on the left, then click “Calculate recommendation”.

    The calculator will suggest deterministic automation, an LLM assistant, or a full AI Agent — and highlight any cost/risk caveats.

    Path 1 – Deterministic automation

    Use workflows, rules and RPA as your primary approach.

    Your use case looks more structured and predictable. Focus on business rules, workflow automation, and RPA. Agents would likely be overkill here, but LLMs can still help with drafting or summarising content.

    Path 2 – LLM assistant + orchestration

    Start with an LLM copilot and light automation.

    Your use case benefits from understanding language and context, but doesn’t strictly require a fully autonomous agent yet. An LLM assistant plus smart workflows can deliver strong value with lower cost and risk.

    Path 3 – Full AI Agent (sweet spot)

    You’re in the AI Agent ‘sweet spot’.

    Complex, dynamic workflows across multiple systems with room for autonomy and learning. This is where a full AI Agent can unlock higher levels of automation and ROI, provided governance and foundations are in place.

    Average capability:/ 4
    Overlay: No major blockers detected.
    Overlay: Caveats found – see notes below.
    • Cost sensitivity is high – compare AI Agent costs carefully against simpler automation.
    • High risk / human judgement suggests keeping a human-in-the-loop, at least in phase one.
    • Tech & data readiness is low – fix foundations or start with a simpler pattern first.
    This tool is a guide, not a rule. iAhub can run a deeper assessment workshop to confirm the right design, architecture, and rollout plan for your organisation.
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