Choosing an AI Agent Platform: A Buyer’s Evaluation Plan With Creatio AI Studio
Key takeaways
- Start an AI agent platform evaluation with a written brief for one workflow: the one that matters most to your business or is hardest to run today.
- Score platforms on how agents get built, connected to your systems and kept under human control.
- Pilot on the difficult cases: sensitive data, exceptions and handoffs to a person.
- Agree the go-live criteria before the pilot starts, so the final decision rests on evidence everyone accepted in advance.
- Each stage shows how the step works in Creatio AI Studio, as a worked example on a real platform.
If you manage projects, there is a good chance someone has asked you to "look into AI agents." The request rarely comes with a budget line or a defined use case, and the vendor demos that follow tend to blur together: an agent reads a message, drafts a reply and updates a record. They all work, because each one follows a script written around a scenario the vendor chose.
Platforms start to differ later, when an agent has to reach the systems your teams already use, follow your approval rules and pass a difficult case to a person without losing context. Those are the moments a buying process should test.
This guide treats choosing an AI agent platform as a project with four stages: scope, shortlist, pilot and decision. Each stage comes with a template you can copy. To show what strong answers look like in practice, each stage also walks through the same step in Creatio AI Studio, the enterprise agent environment from Creatio.
What an AI agent platform should do across the agent lifecycle
An AI agent platform is the software environment where an organization designs, connects, tests, runs and governs AI agents. An AI agent, in this sense, is software that can reason about a task, choose the tools it needs and carry out multi-step actions toward an outcome, within boundaries that people define. The platform supplies everything around the agent:
- connections to business data and applications
- the channels where customers and employees reach the agent
- the controls that set what the agent may do
- the monitoring that shows what it actually did
This definition helps sort the tools that often end up on the same shortlist. A chatbot builder handles conversations but usually stops short of executing work across systems. An automation tool runs fixed rules reliably, while leaving interpretation and judgment to people. A model provider supplies the reasoning engine and typically leaves integration, approvals and monitoring to your team. Each has its place, and on its own each tends to cover only part of the agent lifecycle.
Creatio AI Studio is Creatio's AI-native professional environment for building, running and governing enterprise AI agents. With it, teams can:
is Creatio's AI-native professional environment for building, running and governing enterprise AI agents. With it, teams can:- design agents with natural-language prompts or in no-code visual designers, then equip them with reusable skills and knowledge, or extend them with coding agents such as Claude Code, Codex and Cursor
- connect agents to business systems through APIs and open integrations, including the Model Context Protocol (MCP), an open standard that lets agents use tools and data from other applications
- deploy agents across channels such as email, chat, voice and SMS
- oversee agents with policies, approvals, evaluations and observability
Creatio AI Studio works alongside Creatio AI CRM on a shared platform, and it can also be used on its own, connecting agents to the systems an organization already runs.
Two related parts of Creatio's portfolio often come up in the same evaluation. Pre-built Creatio AI agents for marketing, sales and service come with the CRM. Creatio AI Twin is a conversational, no-code experience that lets employees build personal agents by describing what they need in plain language, with Creatio AI Studio providing the agent capabilities in the background.
With the category defined, the evaluation can begin, and the first stage happens inside your own organization.
Stage 1: Scope one workflow before you meet any vendor
An evaluation without a defined use case tends to drift toward whichever demo impressed the room most. The fix is a one-page brief, written before the first vendor call, covering one workflow. A good candidate meets four tests:
- it happens often
- it follows rules people can describe
- it has a clear owner
- it frustrates people enough that they want it fixed
That brief becomes the script for every demo and, later, the yardstick for the pilot.
Template: AI agent use-case brief
| Field | What to write down | Example: card dispute intake at a bank |
|---|---|---|
| Workflow and owner | The process, and who is accountable for it | Card dispute intake, owned by the head of card operations |
| Trigger | What starts the work | A customer reports a charge they don't recognize or believe is wrong |
| Inputs | The data and documents the agent needs | Transaction details, the customer's description of the problem, supporting evidence |
| Systems touched | Applications the agent reads from or writes to | Card processing system, case management, CRM |
| Decisions the agent makes | Judgments within its remit | Which evidence is still missing; whether the case should go to temporary credit review |
| Actions that need sign-off | Steps a person must approve | Issuing temporary credit; closing a dispute in the bank's favor |
| Channels | Where customers and staff interact with the agent | Web chat, mobile app, email |
| Escalation path | Who takes over, and what context they receive | A dispute specialist, who gets the claim, the checks already run and the actions taken |
| Success signal | How you will judge the pilot | Your own baseline, for example time to acknowledge a dispute or how often evidence is complete at first contact |
Tip: Fill in the "Actions that need sign-off" row with your risk or compliance lead in the room. It is the row vendors will be tested hardest against in Stage 3.
Decide how much autonomy the agent starts with
Once the brief exists, the next decision is how much of the work the agent does on its own. Creatio frames this as three modes:
- Suggest: the agent proposes drafts or next steps, and a person makes the final decision.
- Assist: a person hands the agent a specific task, and the agent carries it out and reports back for approval.
- Act: the agent runs the workflow end to end within defined guardrails, and escalates only the exceptions.
Most teams start supervised and widen autonomy as confidence grows. In the card disputes example, the agent could begin in Assist mode: it gathers the dispute details and opens the case, while every credit decision stays with a person.
Check what already exists before building
The brief also tells you whether you need to build anything at all. Creatio AI CRM includes pre-built Creatio AI agents for marketing, sales and service. One of them, the Case Understanding Agent, condenses long case histories, extracts details such as error codes and product versions, and analyzes the customer's sentiment. For banks and credit unions, Creatio Bank.AI, Creatio's banking CRM and AI platform, brings banking-specific workflows and agents onto the same foundation.
When nothing pre-built matches the workflow, as with card dispute intake, the brief becomes the specification for a custom agent in Creatio AI Studio. With that specification written, every vendor conversation in Stage 2 can begin with the same request: show us this workflow, with these systems and these sign-offs.
Stage 2: Score AI agent platforms on how agents get built, connected and controlled
With the brief written, the shortlist can start. Feature lists tend to look alike at this stage, so the scorecard below rates what each vendor can demonstrate against your own workflow. The weights are a starting point, and your brief should move them: a team whose agent will touch payments, for example, might weight human control higher.
Template: AI agent platform scorecard (score each criterion 1 to 5, then multiply by its weight)
| Criterion | Weight | Ask the vendor to show | How Creatio AI Studio handles it |
|---|---|---|---|
| Build paths for different skill levels | 15 | Your brief's workflow, built by someone with your team's skill set | Natural-language prompts for agent behavior; a Workflow Agent Designer for multi-step logic, business rules and approvals; reusable skills and knowledge; coding agents such as Claude Code, Codex and Cursor for developers; Creatio AI Twin for employees building personal agents |
| Connection to your systems | 20 | The agent reading from and writing to a system named in your brief | A native MCP server exposing Creatio data, records and workflows; custom MCP servers for proprietary systems; APIs; works alongside Creatio AI CRM or connects to another vendor's CRM |
| Model choice | 10 | The model behind one agent being swapped, and what else had to change | Models from OpenAI, Anthropic and Google, public or privately hosted; bring-your-own-model; a model chosen per agent |
| Channels | 10 | One agent continuing a conversation across two channels | Email, web chat, SMS, WhatsApp, voice and video, with context shared across channels; embedding in tools such as Outlook and Teams |
| Human control (human-in-the-loop) | 20 | A step from your sign-off row pausing for approval, then an escalation that hands over full context | Approvals routed in AI Studio, in chat or into the CRM; confirmation required before an agent uses selected tools; defined escalation paths; Suggest, Assist and Act modes |
| Testing and monitoring | 15 | A test run against cases you supply, then the record of what the agent did and why | Evaluations against golden datasets (curated test cases with known correct outcomes) and quality thresholds; run history; an audit log; observability by agent and model |
| Cost visibility | 10 | Where usage appears, and what happens when a limit is reached | Hourly, daily and monthly usage limits with soft-stop warnings; cost broken down by model and agent |
Questions that separate a polished demo from a deployable platform
The scorecard tells you what to watch for. A handful of direct questions moves a vendor from its standard script to your use case:
- "Can you show our workflow, or one close to it, in a follow-up session?" A strong answer is a demo prepared around your brief, usually by the vendor's solutions consultants, so the team can probe the steps that matter to you.
- "What does the agent do when it isn't sure?" Look for a named escalation path and a handoff that carries the complete history, so the person picking it up does not start over.
- "Who decides which actions run without approval, and where is that set?" The answer should point to a policy someone can read and change, rather than to wording buried inside a prompt.
- "If we change models next year, what do we rebuild?" Ideally, very little beyond retesting.
- "How do we know a new version beats the current one before customers see it?" Expect evaluations against a fixed test set, versioning and a way to roll back.
What the card disputes agent looks like in Creatio AI Studio
Turning the Stage 1 brief into a working design shows how these criteria play out. In the Workflow Agent Designer, card dispute intake becomes a visible sequence of steps:
- An intake step gathers the transaction context, the customer's claim and the evidence still needed.
- A tool call creates the dispute case in the bank's systems.
- A sub-agent specialized in temporary credit review takes the case.
- A channel step sends the customer a confirmation and an evidence checklist.
Each step can use the agent's main model or a different one, and the agent can carry the conversation history forward, so the customer never has to repeat the story. Because the logic is laid out visually, the operations owner, a compliance reviewer and a developer can all review the same design.
A scorecard like this usually narrows the field to two or three platforms. Choosing between them takes evidence from your own cases, and gathering it is the job of the pilot.
Stage 3: Run an AI agent pilot built around the cases most likely to go wrong
Vendor demos favor the happy path, where the customer gives clear information and the agent never hesitates. Real workflows are messier. A pilot earns its time by spending most of it on incomplete requests, sensitive data, actions that must stop for approval and questions the agent should not answer at all.
Template: phased AI agent pilot plan (about eight weeks; adjust to the complexity of your brief)
| Phase | Weeks | What happens | Move on when | In Creatio AI Studio |
|---|---|---|---|---|
| Build in a safe environment | 1–2 | Connect the systems in the brief, apply policies and build the agent | The agent completes sample cases end to end | Separate development, test, staging and production environments; organization-wide policies applied to every agent from the start |
| Evaluate against your test set | 3–4 | Assemble real, anonymized cases, including the difficult ones, and agree a quality threshold | The agent meets the threshold on the full set | Evaluations against golden datasets, with quality thresholds checked before deployment |
| Limited release | 5–6 | Release to a small group of staff or customers in Suggest or Assist mode | No open high-risk issues remain, and escalations arrive with full context | Approvals routed in AI Studio, in chat or into the CRM; versioning with rollback to the last working version |
| Review | 7–8 | Compare results with the success signal in the brief; review escalations and the audit trail | The team is ready for the go-live gate in Stage 4 | Run history, audit log, and observability of quality, latency, errors and cost by agent and model |
Test cases worth writing first
The golden dataset carries much of a pilot's value, so it pays to start with the scenarios most likely to break an agent. For the card disputes example:
- Missing information: the customer cannot remember the date or amount of the transaction.
- Sensitive data in free text: the customer types a full card number into the chat. Check that it is masked before the model or the logs ever see it.
- An action that must pause: the case qualifies for temporary credit, and the agent has to wait for a person's approval.
- A request outside the agent's remit: halfway through, the customer asks for a higher credit limit, and the agent should hand off.
- An outage: case management goes offline, and the agent needs a fallback that does not leave the customer guessing.
How Creatio AI Studio applies policies during a pilot
Some rules belong to the whole organization, such as masking personal data or requiring confirmation before an agent uses certain tools. In Creatio AI Studio, these rules are set once and inherited by every agent, and the card disputes team adds agent-level instructions, knowledge and evaluations on top. During the limited release, the governance view shows where policies triggered, which actions were enforced or blocked, and which approvals are still waiting. The pilot team gets a running record of the agent's behavior under real conditions, instead of a summary assembled at the end.
Testing continues after the pilot, too. Results from production feed the next round of evaluation, so the test set grows as the agent meets new situations.
By the end of the eighth week, the team will have evidence from its own cases. The last stage turns that evidence into a decision.
Stage 4: Make the go-live decision against criteria agreed in advance
Pilots that end without a decision rule tend to end in debate. Writing the go-live criteria during Stage 1, and having operations, risk and IT sign them before the pilot begins, keeps the final call tied to evidence rather than to whoever argues most persuasively.
Template: AI agent go-live checklist
Can we see and stop what the agent does?
☐ Every run is traceable: inputs, decisions, tools used and outcome
☐ A named owner can pause the agent or roll back to the previous version
☐ Triggered policies and blocked actions are visible in one place
Does the agent clear the quality bar on our own cases?
☐ The agreed threshold is met on the full test set, including the difficult cases
☐ No high-risk issue from the limited release remains open
☐ Escalations reached the right person with the case history attached
Do we know what it costs to run?
☐ Usage per run is understood, by agent and by model
☐ Limits and warnings are set at levels the budget owner has accepted
Who picks up the work when the agent escalates?
☐ A named team, with agreed hours, owns escalations
☐ Customers know how to reach a person at any point
☐ A regular review of escalations and policy decisions is on the calendar
In Creatio AI Studio, most of these checks map to something the team can open and inspect:
- the audit log records what each agent did and why
- versioning allows a rollback
- the Operations Command Center summarizes live agents, approvals waiting and actions that governance blocked
- usage analytics show consumption against hourly, daily and monthly limits, with soft-stop warnings before a limit interrupts work
Scale autonomy one workflow at a time
Passing the gate starts a second cycle. For the card disputes agent, the next step might be moving routine tasks, such as reminding customers about missing evidence, from Assist to Act mode, while credit decisions stay with people. The same agent can then extend to more channels, such as SMS or voice.
The next workflow starts with less groundwork. That might be fraud alert confirmation, or document collection for know-your-customer checks. Organization-wide policies already apply, skills can be reused, and the team's evaluation habits carry over.
Alongside these enterprise agents, Creatio AI Twin lets individual employees build personal agents in plain language. Each of those agents inherits the same permissions as the person it acts for.
Creatio describes the aim of its AI-native, no-code platform as 10x productivity. For a project team, the practical route toward that kind of gain is a repeatable cycle: brief, scorecard, pilot and gate, applied to one workflow after another.
FAQ: choosing an AI agent platform
What is the difference between an AI agent platform and a chatbot builder?
A chatbot builder designs conversations. An AI agent platform also lets the agent take actions across business systems, such as creating a case or updating a record. Those actions run under policies, approvals and monitoring that the organization controls.
How long should an AI agent platform pilot take?
For a single workflow, about eight weeks is a workable plan, with two weeks each to build, evaluate against a test set, run a limited release and review. Workflows that touch many systems or involve regulated decisions may need longer.
Do business teams need developers to build agents in Creatio AI Studio?
Business and operations teams can design agents with natural-language prompts and the no-code Workflow Agent Designer, while IT governs security, integrations and standards. Developers can extend agents through coding agents such as Claude Code, Codex and Cursor. For personal agents, Creatio AI Twin lets any employee build one by describing what they need.
When should you use pre-built Creatio AI agents instead of building a custom agent?
Pre-built Creatio AI agents are included in Creatio AI CRM and cover common marketing, sales and service tasks. Examples include the Case Understanding Agent, the Root Cause Analysis Agent and the Account Awareness Agent. A custom agent in Creatio AI Studio makes sense when the workflow is specific to your business, crosses several systems or depends on your own approval rules, as card dispute intake does.
What is Creatio Bank.AI?
Creatio Bank.AI is Creatio's banking CRM and AI platform for banks, credit unions and other financial institutions. It shares the Creatio platform with Creatio AI Studio, so banking teams can build and govern custom agents, such as a card disputes agent, with the same designers, policies and evaluations.
How do you know an AI agent is ready for production?
Test it against a golden dataset of real, anonymized cases, including the difficult ones, and set a quality threshold before the pilot starts. Then run a limited release in a supervised mode, and check the results against a go-live checklist that the business, risk and IT owners agreed in advance.
From one workflow to a repeatable evaluation practice
Choosing an AI agent platform goes faster when the evaluation runs like any other project: a defined scope, criteria agreed up front, a test plan built around risk and a decision rule everyone signed. The four templates work with any vendor. Applied to Creatio AI Studio, they show how a single workflow such as card dispute intake can move from brief to governed production, and how the next one can begin with policies, skills and evaluations already in place.
