01
Agent Coordination
A tested starting point for agent coordination reduced repeated setup work.
We built an internal AI operations agent for a growing B2B company to process incoming requests, collect missing information, update business systems, route work, follow up automatically, and flag exceptions for human review.
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A company handling a steady flow of customer requests, internal approvals, documents, follow-ups, and updates across email and multiple business tools.
Coretus mapped the existing process, identified repeatable decisions, built agent workflows, connected systems, and designed clear human approval points.
Help the operations team spend less time copying information, chasing missing details, updating records, and routing routine requests.
An AI assistant that understands incoming work, decides the next step, uses connected tools, and asks for human help only when required.
The operations team spent a large part of each day reading emails, checking attachments, copying details into internal systems, requesting missing information, assigning tasks, and following up on open items.
The process worked when volumes were low, but growth made it harder to keep response times consistent. Hiring more coordinators would increase cost without fixing the underlying workflow, so the company wanted to automate the repeatable parts while keeping people responsible for exceptions and important decisions.
Staff opened each message, identified the request type, and decided who should handle it.
The agent classifies incoming work, captures important details, and routes it based on defined business rules.
Information was manually transferred between email, CRM, spreadsheets, and internal tools.
The agent creates and updates records directly in the systems used by the team.
Staff had to remember which customers, vendors, or teammates still owed information.
The agent tracks open items, sends approved reminders, and alerts a person when something needs attention.
The agent can complete a sequence of actions instead of stopping after one response, helping move work from intake to completion.
The agent connects with the tools the business already uses, reducing the need for teams to switch between systems and repeat the same work.
High-risk, unusual, or approval-based tasks are clearly separated from routine work so automation does not remove necessary human judgment.
Tested foundations helped the team spend less time on setup and more time on the parts that made this product useful.
A tested starting point for agent coordination reduced repeated setup work.
Reusable work for permission controls let the team focus more time on the client’s specific needs.
This made it easier to add workflow tracking without rebuilding common foundations.
A tested starting point for business rule layer reduced repeated setup work.
A straightforward before-and-after view of what changed for the team and their customers.
Routine requests can be understood and moved to the next step as soon as they arrive instead of waiting in a manual queue.
The team spends less time copying data, creating routine records, sending reminders, and moving information between systems.
Instead of reviewing every case, staff can focus on exceptions, approvals, and situations where business judgment is required.
We did not want AI making every decision. We wanted it to handle the repetitive work , keep things moving, and bring our team in when something actually needed attention. That is where the value came from.