Discuss your projectAn agent that can read the order, check the account, and open the ticket saves the walk between systems, but only if its access is deliberate. We build the integration layer that exposes approved actions to the agent, with permissions per role, a person approving consequential steps, and an audit trail of everything it did.
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Why it is happening now
Agent platforms and copilots are now easy to buy, and most of them can call tools. The hard part is what those tools reach: the ERP that holds orders, the CRM that holds accounts, the ticketing system, the practice system, and the older database that nobody wants to touch. Connected carelessly, an agent inherits more access than any single employee has. Connected deliberately, it works through a small set of approved actions that respect roles, stop for approval where the stakes are real, and leave a record of every step.
Illustration of a systems integration built by Baybloom: a CRM, a field application, and accounting kept in sync, with a queue of failed records waiting for review.
What we would do first
We follow a real task from request to finished record and write down every lookup and every change it needs, in which system, and under whose authority.
Each action gets a name, the inputs it accepts, the roles that may use it, its limits, and whether a person must approve it before it runs.
The actions are built against the ERP, CRM, or legacy database in a test environment, and the agent is evaluated on past cases before anyone relies on it.
Consequential actions wait for a person, every call is logged, cost and failures are visible, and the next action is added only once the first proves reliable.
What it looks like after
The agent works through a known list of actions instead of broad system access. It sees what the requesting person is allowed to see and does only what that role may do. Anything consequential waits for approval. Every lookup and change is recorded with who asked, what ran, and what it returned, so a mistake can be traced, reversed, and turned into a test case.
Industrial parts distributor
At an industrial parts distributor, an order question usually means checking the ERP for status, the CRM for account terms, and a carrier site for tracking, then opening a return in the ticketing system. An integration layer exposes read actions across those systems to the copilot the business already licenses, so a representative gets an answer with its sources shown. A next phase adds a single action, creating a return authorization, which waits for a representative’s approval and is logged with the request that prompted it.
The path from here
The service
Custom AI agent developmentCustom AI agents connected to your ERP, CRM, and ticketing, acting inside limits you set.
ReadWhat we build
AI assistants and copilotsYour team gets answers from approved records with the source shown, replies drafted for a person to approve, and routine updates made within limits you set.
ReadA guide
How do you integrate AI agents with existing business systems like ERP and CRM?Written for the decision, not the sale.
ReadQuestions
What companies in this situation ask us first.
It means giving the agent a defined set of actions it can take in the systems you already run, such as looking up an order or creating a ticket, with each action enforcing permissions, limits, approvals, and logging.
Those platforms are good at conversation and at the mainstream systems they connect to. Custom fields, older databases, internal applications, and approval rules specific to your business usually need an integration layer the platform can call.
The Model Context Protocol is an open standard that lets an AI application discover and call tools. An MCP server is one way to expose your approved actions to agents. It helps when several agent tools need the same access; a plain API can be enough for one.
Often, yes. The integration layer can read a database directly with a restricted account, use an export or a message queue, or, as a last resort, drive the screens. Each approach is tested and the choice is written down.
It can only call the actions on the approved list, each action checks the requesting person’s role and the limits, and anything consequential waits for a person to approve it. The agent works with restricted credentials, not an administrator key.
It is evaluated on real past cases before rollout, with the results written down, and monitored after launch for accuracy, failures, and cost per task.
Actions fail safely and report the failure instead of retrying blindly. Every change is logged and, where the system allows, reversible, and the agent can be switched off without stopping the systems it uses.