Start with the outcome when choosing an AI tool: do you need an answer, a reusable method, a finished file or an activity performed in business systems? Then identify the sources, steps and permissions required. The choice becomes clearer when you can also explain how to verify the result.
Chat, custom GPTs, agents and Work operate at different levels and can contribute to the same process. This guide uses ChatGPT terminology and proposes practical IT selection criteria. GPT means a custom GPT here; Work means ChatGPT Work. The business examples are Take Two design proposals, not installed integrations or measured results.
A map for choosing
| Need | Starting point | Outcome to request |
|---|---|---|
| Understand a problem or explore an idea | Chat | Explanation, distinct hypotheses and open questions |
| Apply the same method to different materials | Reusable instructions in a GPT, skill or plugin | Consistent structure and repeatable checks |
| Turn several sources and steps into a completed result | Work | A file, analysis or completed task for review |
| Connect reads and actions inside an application process | An agent with defined tools and controls | An operational result with verification references |
| Apply known rules and calculations | A script or conventional automation | Deterministic output, explicit errors and tests |
This is a selection guide rather than a rigid feature boundary. You can explore in Chat, formalise instructions and execute in an operational environment. The model, available tools and review process are separate choices.
Use Chat to understand and prepare a decision
Chat suits questions, exploration, short drafts and discussion. OpenAI describes Work as the option for delegating a task with a reviewable result. Both modes can use plugins: check actual availability rather than inferring it from the mode name. Official Chat and Work walkthrough.
For a sales-upload timeout, ask for an investigation checklist. Supply the message, service, time and evidence already collected. Request a table separating symptoms, hypotheses and further evidence needed. The expected result is an investigation path; the system evidence must still establish the cause.
Chat may be sufficient when you own the next step and do not need extensive retrieval or a substantial deliverable. A focused conversation can also establish which missing input must be obtained before considering automation.
Use GPTs and reusable instructions to stabilise a method
A custom GPT combines instructions, references and capabilities for recurring work. Current documentation describes a transition to plugins and skills; custom actions require separate rebuilding. Check the available workspace path before designing a new solution around this feature. Official migration guidance.
Suppose incident notes should always contain five sections: symptom, timeline, completed actions, observed outcome and open questions. Save the instructions, examples and acceptance criteria instead of rewriting them each time.
Expect greater consistency in the method, tested on actual cases. Outdated references or ambiguous instructions can produce consistently wrong answers. Assign an owner and maintain the source procedures.
Use Work to reach a completed result
Work suits multi-step assignments with reviewable outcomes, using files, plugins and approved tools. Desktop tasks can also use local resources when available. Actual capabilities depend on the plan, environment and configuration. Official introduction to ChatGPT Work.
A concrete assignment could be: use the CSV files in this folder to create the daily report; validate expected tills, duplicates and revisions; produce HTML and a management summary with source references; stop before sending email.
Expect files, recorded checks and unresolved issues. Without a source-system connection, the task may only use supplied files: describing the desired integration does not create it. Recurring runs also need scheduling; creating a report today does not schedule tomorrow's report.
Use agents to connect tools and actions
An agent combines a model, instructions and operational capabilities, choosing steps within its environment's controls. OpenAI recommends starting with a focused agent and adding specialists where ownership, tools or approval policies differ. Agent definitions.
For monitoring tickets, an agent can prepare text and call an authorised integration. The workflow must still validate the event, identify the episode and retain the resulting ticket ID. Success means a ticket actually exists and is correlated with the right event.
Conventional code can coordinate fixed sequences. Use agentic interpretation for variable material, tool selection or exceptions. Summing CSV figures, validating fields and choosing revisions remain suitable deterministic functions.
When to chain tools
A chain passes each stage's result to the next. It helps when stages make verification easier, limit permissions or separate work with different acceptance criteria. Not every stage needs an agent.
You can begin with a manual handover of verified results. Automatic handovers require an implemented coordinator, workflow engine or available product capability. Selecting several GPTs or opening conversations does not itself create an automatic chain.
The examples below are design proposals. OpenAI's multi-agent guidance distinguishes specialists that take ownership from bounded specialists called by a coordinator. Orchestration and handoffs.
Till report example
- A read-only connection retrieves summaries and expected tills.
- Code validates input, resolves revisions and calculates coverage and amounts.
- AI writes commentary from those results, preserving missing data.
- A check compares dates, amounts and scope with the report.
- Distribution follows a separate rule with defined recipients and permissions.
The report calculation can remain useful when AI commentary fails. The 295 receipts in our example should travel with their definition and three-out-of-four coverage, rather than being recalculated by each agent. Try the deterministic part in the till report CSV lab.
Monitoring ticket example
The receiver authenticates the event and identifies its episode. AI prepares the description, while defined rules determine assignment and priority. The connector creates or updates the ticket and the workflow verifies the result. The description component does not need ticket deletion or routing-administration privileges.
Retries after timeouts must reconcile a ticket that may already exist. Retain a stable episode identifier and distinguish a missing response from an action that never happened. Worked ticket-opening example.
Monitoring plugin example
Chat helps clarify the measurement. A work environment drafts code and tests from the specification. Tests cover thresholds, output and missing data. Review checks implementation against requirements, while deployment uses separate destination permissions.
The authoring component can write to a development directory without receiving production administrator access. For a prototype assignment, success is verifiable code; it does not mean the check is already installed. Monitoring plugin guide.
Define stage contracts
Pass structured information with explicit meaning and state. A report contract can include period, currency, amount units, expected and complete tills, available results, source references and run identifier. A ticket contract can include episode, asset, symptom, evidence and operation state.
Define how downstream stages handle an incomplete contract. Missing is not zero, a hypothesis is not a confirmed cause, and an attempted creation is not an opened ticket. Each stage checks the prerequisites it needs.
Specify error handling too: bounded retries, deadlines, review queues and an owner. End-to-end duration includes recovery, approval and correction as well as generation. Additional components create more failure points; separate stages where doing so makes the workflow more controllable.
Assess permissions
Consider tool availability, connected identity, source-system rights and action permissions separately. Making a plugin available does not connect an account. A shared connection can have different rights from the requesting user. Connections and access in Work.
The following are Take Two pilot recommendations, to compare with controls actually supported by the product and connected applications.
| Activity | Suggested initial access | Condition for expansion |
|---|---|---|
| Understand an error | Relevant excerpts supplied by the user | An additional required source is identified |
| Create a till report | Read required summaries, write to an output folder | Distribution rules and recipients are defined |
| Generate a plugin | Development directory and test environment | Tests and review pass; deployment is authorised |
| Prepare a ticket | Read the event and prepare a draft | Assignment, duplicates and failures are handled |
| Send or publish | Specific destination and verified content | Explicit approval or an operational rule |
Ask what data can be read, which objects can be changed and where results can leave the system. Define access duration, revocation and who verifies actions. Reading can expose sensitive information even without modification, so source scope matters.
An instruction not to delete does not technically remove deletion capability. Where supported, constrain identities, directories, environments and operations. Assign ownership, execution scope and stop conditions for recurring work.
Approval should concern a concrete action: content, destination, affected objects and impact. Automatic controls validate inputs or operations; human review decides whether to permit a sensitive transition. OpenAI guardrails and human review.
Retrieved emails, pages and logs remain material to analyse; they do not authorise new actions. A second agent is not automatically an independent reviewer and can reproduce the same mistake. Add original evidence, tests and destination-system verification.
Measure the result
Compare with the current process using normal cases, missing data, duplicates, failures and disallowed actions. Measure remaining correction effort and whether outcomes are verifiable. Avoid claiming a savings percentage without measurement.
| Work | Proposed acceptance criterion |
|---|---|
| Explanation or diagnosis | Relevant claims have evidence or are labelled hypotheses |
| Report | Amounts, period and coverage match the sources |
| Plugin | Tests cover requirements and failure cases |
| Ticket opening | The right correlated ticket exists without duplication |
| Sending or publishing | The destination received the approved version |
Record total duration, corrections, failures, duplicates and workflow costs, including integrations, retries and human review. Repeat important cases after model, instruction or integration changes. OpenAI documents traces and evaluations for inspecting tool choices, handoffs and regressions in API workflows. Agent workflow evaluation.
Choose the first step
Define an outcome and an acceptance criterion. Start in Chat for discussion; save instructions when the method recurs; consider Work for multi-source execution; define an agent's tools and controls for application workflows. Keep exact-rule steps in code.
Build the simplest verifiable path first. Add stages for concrete needs: sources, specialist work, calculations, access boundaries or approvals. Aim for a process the team understands and can operate, correct and maintain.
Begin with the structured prompts course or the practical IT articles. Product descriptions refer to documentation consulted on 13 October 2026; verify availability and controls in your plan and workspace.