AI workflow buyer’s guide
AI agents for business: a practical buyer’s guide
Start with the work that keeps getting stuck. Decide whether an agent is a good fit, then define the outcome, boundaries and evidence before appointing a development partner.
Is your workflow suitable for an AI agent?
An AI agent may be useful when a recurring task needs changing steps, interpretation and controlled access to tools. It is not automatically the right answer to repetitive work. If the rules are clear, a form, integration or conventional workflow may solve the problem more simply.
Before appointing a development partner, define the first useful outcome, the information it depends on, the actions it may take, who approves exceptions and how you will judge success. Start with the smallest release that solves a real problem. Expand only when the evidence supports it.
01
Start with the friction, not the technology
Enquiries arrive without enough detail. Someone searches several systems before replying. Documents wait for the right person. Staff re-enter the same information or chase an approval that has no clear owner. These are useful starting points for a discussion, but each may need a different solution.
Ask where work stops, what people do to move it forward and why that step is difficult. Then choose the simplest approach that can reliably deliver the outcome.
On smaller screens, scroll the table sideways to read each column.
| What the work looks like | Useful starting point | What to establish first |
|---|---|---|
| Known rules and repeatable steps | Conventional workflow automation | The trigger, business rules and exception owner |
| Questions answered from maintained information | A search or conversational assistant | The approved sources and when to stop answering |
| Variable steps requiring several tools | A bounded agent pilot | Why fixed steps are insufficient and how actions will be checked |
| Missing information or contradictory ownership | Process and data preparation | Who can resolve the underlying uncertainty |
| Decisions with serious consequences for people | Human-led assessment and specialist review | Whether AI assistance is appropriate at all |
An agent differs from a conversational interface because the model can choose steps and tools. Anthropic’s architecture guidance distinguishes this from predefined workflows and recommends adding complexity only when it improves the outcome. Our AI agent versus chatbot guide explores that choice in detail.
02
Describe one first useful outcome
“We want AI in the business” is not a delivery brief. “Give the service team a complete, correctly routed enquiry to review” is much closer. It identifies an output, a recipient and a point where a person can check the work.
- Trigger: what starts the task, and who is asking?
- Current work: which steps consume time, create mistakes or leave customers waiting?
- Completion: what must be true before the task counts as finished?
- Boundary: what is deliberately outside the first release?
- Baseline: how will you compare the new workflow with the current one?
Measure the outcome your team can use, not just the number of messages an agent handles. A drafted response that still needs extensive correction is not the same as a resolved request. Record current handling effort, delays, rework and handovers before a pilot so the comparison has a starting point.
03
Bring evidence about the data and systems
List where the necessary information lives, who maintains it and who may see it. Include examples of incomplete, conflicting and outdated records. Use approved, redacted samples during early scoping rather than sharing live customer records or credentials.
For each proposed connection, ask your partner to confirm the actual system version, available interface, required permissions and vendor restrictions. Separate reading information from drafting a change and executing it. An API being available does not establish that the required operation is supported, affordable or permitted.
Ask for a small connection test before treating an uncertain integration as a committed build dependency. Agree what happens when the system is unavailable, information is stale or the same request arrives again. The AI agent integration guide covers the technical contract behind these questions.
Map what information reaches each supplier, where it is processed and retained, and who can access or delete it. Have your responsible privacy and security reviewers check the proposed handling against your obligations and policies. Do not treat a provider setting or a promise of “secure AI” as a completed review.
05
Agree how you will accept the work
A polished demonstration shows a possibility. Acceptance needs evidence from the situations your team actually encounters. Bring anonymised examples of ordinary requests, ambiguous wording, missing information, exceptions and previous mistakes.
Ask the development partner to turn these into repeatable tests with expected outcomes. Include unavailable systems, duplicate requests, attempts to access another customer’s information and instructions embedded in documents that the agent should treat as data, not authority.
- Can the right user complete the intended task?
- Does the workflow stop or hand over when it lacks the information or authority it needs?
- Are approvals and the resulting actions visible to the responsible team?
- Can a failed or incorrect action be contained and recovered?
- Does the outcome justify the total handling effort and operating cost?
Agree acceptance criteria with the people who will use and own the workflow before implementation. Avoid a single “accuracy” score that hides serious failures. Decide which failures block release, who signs off and how you will repeat checks after changes.
06
Plan the running service, not just the build
Someone must own source updates, unanswered requests, access changes, supplier changes and incidents after launch. Ask who watches for failures, who can pause the agent and how staff continue working if it is unavailable.
Track useful completions, correction effort, escalations, response delays and usage alongside failures. Logs should help investigate problems without unnecessarily retaining sensitive content. Agree access and retention with the relevant reviewers.
Cost and delivery time depend on the scope you agree with your team and partner. Separate discovery and implementation from model usage, messaging, connected-system fees, hosting, monitoring, support and human review. Ask which costs vary with volume and what limits or alerts apply.
Ask how AI-assisted development will be used and reviewed. It may support delivery tasks, but it is not a reason to skip integration checks, acceptance testing or operational preparation. Estimate the actual first release rather than assuming a standard saving.
The WhatsApp AI-agent cost guide illustrates channel-specific cost questions. For a web or internal workflow, use the same scoping discipline without assuming the same supplier charges.
07
Your pre-appointment brief
Use this as a working brief, not a test you must pass before speaking to a developer. If something is unknown, make it an explicit discovery question with an owner and a decision point.
On smaller screens, scroll the table sideways to read each column.
| Decision | Bring from your business | Ask the partner to provide |
|---|---|---|
| Problem and first release | One workflow, affected users, current friction and desired output | A bounded scope, exclusions and simpler alternatives considered |
| Information | Source owners, approved examples and known quality gaps | A data-flow map and a plan for missing or stale information |
| Connections | System names, versions, vendor contacts and access constraints | Evidence that the necessary interface and operations are feasible |
| Authority | Permitted actions, prohibited decisions and accountable approvers | An action-and-permission matrix and demonstrated approval controls |
| Acceptance | Representative examples, failure consequences and a sign-off owner | A test plan, results, release criteria and recovery approach |
| Operations | A day-to-day owner and existing support expectations | Monitoring, escalation, pause and maintenance responsibilities |
| Commercial handover | Budget constraints, procurement requirements and ownership expectations | Scoped estimates, assumptions, supplier costs, account ownership and exit arrangements |
Before committing to a full build, ask which assumptions have been tested and which remain open. Where uncertainty is material, agree a focused discovery or proof-of-feasibility step first. The output should be a decision you can use, including a recommendation not to proceed if the workflow is a poor fit.
08
A smaller first release can still solve the problem
Hypothetical example, not an LCR case study: a service team receives incomplete enquiries and spends time finding the correct department. A first release could collect missing details, prepare a summary and propose a destination for staff review.
The boundary is explicit: it cannot quote, promise availability, approve an exception or change a customer account. Its sources are approved service information and routing rules. Unknown requests go to a named person rather than receiving an invented answer.
During a pilot, the team compares routing correctness, missing information and correction effort with its current process. If a form and fixed rules achieve the same result, there is no reason to add an agent. If varied requests genuinely need interpretation and tool selection, the next discussion is about that narrow capability, not automating the entire business.
09
Discuss the workflow with LCR
LCR’s AI Agents page describes enquiry qualification and routing, support triage, document intake and internal knowledge workflows, alongside WhatsApp, web and voice/outbound experiences. These are starting points for a scope discussion, not a guarantee that every system, channel or use case is supported.
Bring the work, the information source and the action boundary. Confirm the specific delivery scope, dependencies and necessary specialist involvement with LCR before committing.
Start with the four-question AI agent use-case check. The initial assessment does not require contact details; requesting a follow-up action plan asks for your name and email. If the problem is broader than AI, use the general workflow check instead.
Sources
Evidence and limits
Reviewed on 4 September 2026. This is a buyer-preparation framework, not a certification, a project estimate or confirmation that a particular use case meets legal or regulatory requirements.
- LCR Technologies: AI Agents, the owned source for the general offer described here.
- Anthropic: Building effective agents, the workflow-versus-agent distinction and simpler-first design principle. Its tooling examples are not treated here as a current product specification.
- OWASP: AI Agent Security Cheat Sheet, technical guidance on permissions, oversight and security testing.
Questions
Frequently asked questions
How do I know whether my business workflow is suitable for an AI agent?
Look for a recurring, valuable task with a clear outcome, available information and variable steps that simpler automation cannot handle well. You also need an accountable owner, explicit limits and a way to test the result. Repetition alone does not justify an agent.
What should I define before appointing an AI development partner?
Define the workflow, intended users, first useful outcome, information sources, system access, permitted actions, human approvals, acceptance evidence and ongoing owner. Unknowns can become a bounded discovery brief rather than assumptions hidden inside a build quote.
Do I need to replace my existing business systems?
Not necessarily. A proposed workflow may use an approved connection to an existing system, but access, vendor restrictions, data quality and recovery must be checked. A generic integration claim is not proof that your particular system and required actions are supported.
Can an AI agent make higher-risk business decisions?
Keep higher-risk decisions human-led. Ask the relevant specialists to assess the use case before implementation, and define the authority, evidence and approval required for each consequential action. Human review alone does not establish that a use case is suitable or compliant.
How much will an AI agent cost and how long will it take?
Both depend on the first release, information quality, integrations, risk controls, testing and ongoing support. Work with your team and development partner on a scoped estimate. Agree the smallest useful release first, then plan expansion from observed results.
Can I start the LCR assessment without leaving contact details?
Yes. The initial four-question AI agent use-case check can be completed anonymously. Requesting a follow-up action plan is optional and asks for your name and email.
Find a useful first step
Bring the workflow that keeps slowing your team down.
Start with four questions about the audience, work, information and action boundary. Complete the initial assessment anonymously, then choose whether to request a follow-up action plan.