The strongest AI-agent partner is not the one with the loudest demo. Look for a team that can explain the business workflow, data boundaries, model/tool architecture, failure handling, human approval and how the system will be tested before production.
Discuss your requirementSee engineering proofWrite the job in one sentence: what triggers the agent, what information it may use, what action it may take, and when a person must approve. This makes vendor comparisons much more useful than asking for “an AI agent” in the abstract.
A production agent normally needs more than an LLM: identity, retrieval or approved context, tool permissions, logging, error handling, evaluation and a human escalation path. Ask the vendor to show those pieces clearly.
Can the team turn a messy business process into clear triggers, steps, exceptions and measurable outcomes?
How will documents and knowledge be selected, cited, refreshed and access-controlled? A good demo should distinguish sourced answers from model guesses.
Agents that touch CRM, email, documents or payments need allow-lists, role boundaries and review gates. “It can do anything” is not a production feature.
Ask for test cases covering correct answers, refusal cases, missing data, provider failure, timeouts and handoff to a human.
Production teams need logs and traceable states so a failed action can be investigated rather than silently retried forever.
Clarify where data is stored, which providers receive it, how secrets are protected and what is excluded from model context.
Separate implementation cost from recurring model/API, messaging, hosting and support charges. Compare the written scope rather than only the headline number.
Agree source ownership, deployment notes, environment configuration, acceptance criteria and support boundaries before the full build starts.
A paid pilot is valuable when the workflow has uncertainty. Keep it narrow: one data source, one or two approved tools, a small evaluation set and a defined success metric. A pilot should reduce risk, not become an endless prototype.
KK Cyber’s public demos are labelled as simulations unless a feature has been locally or provider tested. That distinction is useful when comparing any vendor.
Use “best” lists as discovery, not proof. Evaluate workflow fit, architecture, security, evaluation evidence and handover terms for your exact use case.
A chatbot mainly handles conversation. An agent may also plan steps and use approved tools to perform actions, so permissions, logging and human review become more important.
Ask for both separately. Implementation is usually a project cost, while model/API, messaging, cloud and support costs may recur.