AI Continua / Custom Agents
Agents that do real work inside your rules
We build and train AI agents on your processes, documents and systems. Each one has a defined job, limited permissions, human approval where it matters, and a complete record of what it did.
What we mean by training
Training an agent is mostly about knowledge, tools and boundaries
Fine-tuning a model is one option, and often not the first one. Most business agents perform best through good retrieval, clear instructions, well-designed tools and rigorous evaluation. We choose the lightest approach that meets your accuracy and risk targets.
- Knowledge: curated, permissioned retrieval over your policies, tickets, contracts and runbooks.
- Behavior: role, tone, escalation rules and decision criteria written down and tested.
- Tools: narrow, well-described actions against your systems rather than open-ended access.
- Model adaptation: fine-tuning or distillation only where evaluation shows it pays off.
Where agents help
Start with work that is repetitive, rule-bound and measurable
IT service desk and operations
Triage tickets, reset access with approval, summarize incidents, draft runbook steps and route to the right team.
Knowledge and policy assistants
Answer employee and customer questions from approved documents, with citations and respect for who is allowed to see what.
Document and claims processing
Extract, validate and route information from invoices, contracts, forms and claims, flagging exceptions for people.
Recruiting and workforce support
Screen resumes against defined criteria, schedule interviews and prepare candidate summaries. Decisions stay with people.
Engineering and QA assistants
Generate test cases, review pull requests against your standards, triage failures and keep documentation current.
Analytics and reporting agents
Turn plain-language questions into governed queries and scheduled reports, using your approved data definitions.
Built to be trusted
Controls designed in from the first sprint
Because AI Continua also audits agents, we build ours to pass the tests we would run against anyone else's.
Least privilege
Each tool is scoped to the minimum action, dataset and credential it needs.
Human approval
Payments, deletions, external messages and other sensitive actions wait for a person.
Evaluation first
A test set of real tasks and adversarial cases gates every release and model change.
Full audit trail
Every prompt, retrieval, tool call and decision is logged for review.
How we deliver
From one well-chosen use case to a running agent
Pick the job
Choose a workflow with clear inputs, outputs and a way to measure success.
Design
Define the agent's role, knowledge sources, tools, approvals and risk controls.
Build and evaluate
Develop against a task test set. Red-team the agent before anyone outside the team uses it.
Pilot
Run with a small group, human review on, and compare results with the current process.
Operate
Monitor quality and drift, retrain knowledge, expand scope as the evidence supports it.
Enablement
Your team should be able to run what we build
We hand over documentation, evaluation suites and runbooks, and train your people to maintain the agents, update knowledge and review agent activity. If you need more hands, MentourCorp can also staff AI engineers, data engineers and QA automation specialists to work alongside your team.
Team training
Hands-on sessions for engineers, analysts and security staff on building and reviewing agents.
Runbooks
Clear procedures for updating knowledge, handling incidents and rolling back changes.
Evaluation kit
Test sets and scripts so you can measure quality yourself after launch.
Staff augmentation
Vetted AI and data professionals through MentourCorp's talent practice.
Agent questions
Which models and platforms do you use?
We choose per use case based on accuracy, cost, latency and data requirements, including hosted and privately deployed options. We avoid locking you to one provider where we can.
Will the agent learn from our data without permission?
Only as designed. We document what data is used for retrieval, evaluation or fine-tuning, and configure vendors so your data is not used to train their general models unless you choose that.
How do you measure whether an agent is good enough?
We build a task test set with you before development, define pass criteria, and track results on every release. Pilot results are compared against your current process.
What happens when the agent is wrong?
Approval gates and logging limit the impact. We define escalation paths, confidence thresholds and rollback steps up front, and review failures as part of regular operation.
Tell us the job. We will tell you if an agent fits.
Bring one workflow you would like to automate. We will outline the approach, risks and how to measure success.