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Flagship field guide

MDX

How to Choose a Good Agentic AI Use Case

Find work where adaptive decisions create value and errors remain detectable, recoverable, and bounded.

4 min readAgentic Systems Editorial Team

Editorial review: clarity, operational relevance, safety boundaries, and source quality.

Look for variable paths

Strong candidates involve changing inputs, multiple systems, and decisions that cannot be captured by one fixed sequence. Repetitive deterministic work is usually better served by ordinary automation.

Map the work before choosing the technology. Good candidates have variable inputs, paths that depend on discovered information, and tools that can expose the required state. If every branch is already known, a workflow will be simpler to test, operate, and explain.

Prefer verifiable outcomes

The system should be able to check whether the task was completed using evidence such as tests, record state, or human confirmation. Subjective outcomes require more review.

Define an observable scorecard: completion state, required evidence, allowed actions, human corrections, time saved, and cost. Tasks based entirely on subjective approval are difficult to improve because the system cannot distinguish a successful strategy from a persuasive output.

Bound the downside

Begin with read-only, reversible, or draft-producing work. Expand autonomy only after evaluation and production data show that controls work under real conditions.

Assess downside through consequence, reversibility, data sensitivity, frequency, and exposure to adversarial input. Begin with research, drafts, or reversible changes. Narrow scope is a product advantage: it speeds evaluation and lets teams expand authority from evidence rather than assumptions.

Practical example

Comparing two finance candidates

Automatically categorizing known invoice fields follows stable rules and is best as a workflow. Investigating invoice exceptions requires checking purchase orders, communications, and supplier records in a variable order, so it may benefit from an agent. The first agent release gathers evidence and recommends a resolution; payment changes remain outside its authority until reliability is proven.

Field checklist

Apply it in practice

  • Map variability, systems, and decision points.
  • Confirm the outcome can be independently verified.
  • Compare against a workflow and manual baseline.
  • Start with a narrow, reversible capability.

Decision framework

Questions to answer before you build

The best use cases combine variable paths, accessible tools, verifiable outcomes, and bounded downside. They are discovered by analyzing work—not by adding chat to a product.

Does work require adaptive sequencing?

Choose tasks where evidence changes the next step. Use ordinary automation when inputs and branches can be specified in advance.

Can the outcome be independently judged?

Prefer tasks with record state, tests, sourced facts, policy checks, or structured human confirmation.

Can downside be bounded?

Start with read-only research, drafts, reversible changes, limited data, and clear escalation before expanding authority.

Common failure signals

Watch for these warning signs

  • Selecting an emotionally impressive but unmeasurable use case.
  • Ignoring the cost of obtaining clean tool access and authoritative data.
  • Automating a broken process without redesigning ownership and exceptions.

Field manual

Implementation blueprint

  1. 01

    Observe the real work

    Sample completed cases, exceptions, handoffs, systems used, wait time, rework, and decision points.

    Deliverable: A workflow map grounded in actual cases rather than an idealized process document.

  2. 02

    Locate adaptive value

    Identify where unstructured evidence or changing conditions make fixed rules expensive or brittle.

    Deliverable: A narrow candidate decision, not a promise to automate the whole role.

  3. 03

    Test system readiness

    Confirm authoritative data, narrow tools, stable identity, and independent outcome checks exist or can be built.

    Deliverable: An integration and evidence gap list included in the business case.

  4. 04

    Compare alternatives

    Prototype the manual baseline, deterministic workflow, model-assisted workflow, and bounded agent where reasonable.

    Deliverable: A decision based on verified outcome quality, time, cost, and correction—not demo appeal.

Reusable working artifact

Use-case portfolio rubric

Score each dimension from 1–5, but treat hard constraints as gates rather than letting a high total conceal unacceptable risk.

CANDIDATE: [bounded job]

VALUE
Adaptive sequencing needed                         1 2 3 4 5
Unstructured evidence is central                   1 2 3 4 5
Frequency or delay creates material cost           1 2 3 4 5

FEASIBILITY
Authoritative data is accessible                   1 2 3 4 5
Required actions can be exposed as narrow tools    1 2 3 4 5
Outcome can be independently verified              1 2 3 4 5

CONTROL
Mistakes are reversible or approval-gated          1 2 3 4 5
Sensitive data and permissions can be scoped       1 2 3 4 5
A team owns quality and incidents                  1 2 3 4 5

HARD GATES
[ ] lawful and policy-permitted use
[ ] credible source of truth
[ ] acceptable worst-case effect
[ ] usable escalation path

NEXT TEST: [smallest assumption the prototype must validate]

Measurement

Operational scorecard

Outcome deltaQuality, time, and cost improvement over the best non-agent baselineAn agent should earn complexity through a whole-task advantage.
Exception burdenShare of cases handed off and time needed to repair contextA high handoff rate can erase apparent automation value.
Integration costWork required to expose authoritative data, tools, identity, and controlsInclude this in the business case before judging model feasibility.

Failure drills

Rehearse before the system has real authority

  • Remove agentic routing and compare with a fixed model-powered workflow on the same cases.
  • Run a two-week shadow test using real case distributions without allowing external effects.
  • Review the worst-scoring risk dimension separately instead of relying on the total score.

Selected primary references

Continue with the source material

These sources inform the wider editorial perspective for this topic. They are not presented as line-by-line citations for every statement.

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