Operations agents
Gather information across systems, prepare updates, route exceptions, and hand off the cases that need judgment.
Give an agent a real job: read the right context, use the right tools, take the next step, and ask a person when the decision needs one. We build custom AI agents around your workflow and connect them to the software your team already uses.
For internal operations, customer-facing products, and AI features inside your app. Native engineering for the hard parts: the data, integrations, permissions, and failure cases that a demo skips.
Not a model name. These are common starting points; the tools, permissions, and review steps depend on your systems.
Gather information across systems, prepare updates, route exceptions, and hand off the cases that need judgment.
Find the right answer from approved sources, draft a response, and escalate sensitive or unresolved cases.
Search your documents, compare sources, and show where an answer came from instead of inventing one.
Add guided workflows, contextual help, or task completion to the app your customers already use.
These examples show different jobs an agent can take on. They are demonstrations, not a promise that the same design fits every business. We build custom agents around your systems, permissions and review steps.
Generate and review platform-specific video content from a topic or hook, using a personal or default avatar. The supplied walkthrough covers LinkedIn, Instagram, YouTube Shorts and X.
Connect a GitHub repository, select an issue, follow the agent's planning and execution, then review the resulting pull request and policy checks before merging.
Enter income, expenses, loans and goals through a guided conversation; review long-term projections and alternate scenarios, then download a plan.
Illustrative planning, not personalized financial advice.
Have a different workflow? Tell us what should happen, which systems are involved, and where a person needs to approve the next step. We will scope a custom agent around that job.
Every agent is built around your systems, not a generic template.
Connect to the APIs, databases, files, and business systems that the task actually needs.
Make the relevant information available at the right step, with boundaries around what each user and agent can access.
Define when an agent can act, when it should stop, and which actions need a human approval.
Test realistic tasks and failure cases; log decisions, errors, latency, and running costs so the system can be improved.
Deploy into the agreed environment and document how your team operates, monitors, and changes the workflow.
We define what success means before we write the first line of the agent.
The input, systems, permissions, expected output, exceptions, and the person who owns the decision.
Choose the model and tools for the job, then set access limits and approval points.
Against real examples, including bad inputs, missing data, tool failures, and cases where the agent must stop.
Review results, costs, and failure patterns before expanding its scope, with a clear handover.
No vibe-coded prototype dressed up as a production agent. No reskinned white-label system pretending to fit your workflow. We engineer the data flow, tool access, orchestration, tests, and deployment around the work you need done. The result should survive missing data, failed integrations, and decisions that need a human.
An agent should not get broad access just because a task sounds simple. We scope its tools and data, set boundaries for external actions, and keep a person in the loop where the stakes require it. We evaluate on your workflows before production, not just on a polished example. When a simpler rules-based workflow is more reliable or less expensive, we will say so during discovery. AI belongs where it improves the work.
If something is missing, email info@matrytech.com, a senior engineer replies.
Bring the workflow, the tools your team uses, and the point where the work gets stuck. We'll help decide what should be automated, what needs approval, and what it takes to put the agent into production.