AI operations and engineering
Custom AI Agent Development
Put an AI agent to work on a specific operational job. Suede AI turns your rules, source material and tool access into a tested workflow with clear outputs and a person responsible for exceptions.
What your team receives
- Agent requirements and guardrailsA written job definition covering inputs, outputs, access, approvals and escalation ownership.
- Implemented agent workflowThe scoped task logic and connections, configured around the operation rather than a generic chat conversation.
- Evaluation cases and handoverExamples showing expected behavior, recorded checks and an operating guide for the responsible team.
A defined scope, an accountable owner and a useful handover.
Give the agent a job you can evaluate
Start with a recurring task that has a recognizable beginning and end. Describe the incoming material, the decision to make and the result a teammate needs. A useful specification might ask an agent to assess an inquiry, explain its assessment and prepare a handoff for review.
We work through representative examples with the process owner. Approved reference material, successful past outputs and awkward edge cases become the basis for the build. That gives the team a practical way to judge behavior before the agent reaches live work.
Connect decisions to the right tools
The agent specification records which information it may read, which systems it may update and which actions need approval. Tool permissions follow that scope. Each output carries the context the next person needs, including the source information behind a recommendation.
Missing inputs, conflicting records and failed connections get explicit handling. The workflow can request clarification, leave a review item or stop an affected action. The designated owner sees what remains unresolved and can decide the next step.
Accept the build against real task cases
Suede AI evaluates agreed examples across ordinary runs and exceptions. We compare the output with the written criteria, inspect tool actions and check whether the right person receives a handoff. Failures become specific revisions to the prompt, logic or connection.
The handover includes the accepted task scope, evaluation examples and operating notes. Your team can see how the agent is meant to behave and which changes require a new check. Ongoing prompt tuning and run review can then be scoped through managed AI agent maintenance.
How the work is delivered
- Specify Choose the task owner, input examples, permitted actions and acceptance criteria. Resolve ambiguous business rules before implementing them.
- Build Implement the agent workflow and the agreed tool connections. Make approval points and exceptions visible to the person running the process.
- Evaluate and hand over Run the acceptance cases, resolve material failures and document how to operate the approved workflow.
Example: preparing an inquiry handoff
Illustrative example
An agent reads an approved inquiry record, checks stated fit criteria and drafts a reasoned handoff. An incomplete inquiry enters a review queue; the agent does not fill missing business facts with guesses. The sales owner reviews uncertain cases and approves any expansion of its actions.
Questions about Custom AI Agent Development
How is an agent different from a chatbot?
A task-focused agent follows a defined workflow and may use approved tools to produce an operational result. A chat interface can be part of that workflow. The engagement defines the actual job, permitted actions and evidence needed to accept the output.
What can the agent do without approval?
Only the actions agreed in its operating scope and allowed by its connected tools. We identify approval points before launch and test them. Changes to permissions or consequential actions require the designated business owner to approve the revised scope.
Define the first agent job
Bring one recurring task, sample inputs and the name of its owner. Suede AI will help turn them into a build scope and clear acceptance criteria.