AI agent development services with the guardrails enterprises need
We build AI agents that complete multi-step work inside your systems: reading, checking, drafting, matching and routing. Every agent has tight permissions, a person approves anything irreversible, and every step is logged.
- An extraction agent live in a claims platform
- Approval before any irreversible action
- AWS, Azure or Google Cloud, your choice of model
Work AI agents can take off your team's plate
Narrow, repetitive, multi-step work, where a person can check the result.
Document-heavy workflows
Intake, extraction and checking against source pages and business rules.
Intelligent document processingDrafting with review
Agents that prepare letters, summaries or reports, then check them against a rubric before a person signs off.
Payments and reconciliation
Matching payments to records, investigating differences and proposing postings for approval.
Triage and routing
Classifying requests and cases and routing them to the right queue, with the context attached.
Answers across systems
Agents that look things up in your documents and data to answer questions with sources.
AI knowledge assistantsFollow-up and chasing
Requesting missing information and following up on overdue items, within limits you set.
How a multi-agent system fits together
Specialist agents, each with one job, coordinated by an orchestrator and stopped by people where it matters.
PlansOrchestrator
Breaks the job into steps and hands each to the right agent.
DoesSpecialist agents
Each one reads, checks, drafts or matches, and nothing else.
UsesYour tools
APIs and databases, with access limited to what each agent needs.
StopsApproval gate
A person approves anything that cannot be undone.
RecordsSystems and logs
Results land in your systems of record, with a full audit trail.
Guardrails first, autonomy second
The controls that make agents safe to run in production.
- Least-privilege access: each agent reaches only the tools and data its job needs
- Approval before any action that cannot be undone, such as posting a payment or sending a letter
- Limits on steps, retries and spend for every run
- Agents check their own output against a rubric or the source before handing it on
- A full log of every step, decision and cost
- A clear fallback: when an agent is unsure, the work goes to a person
How we test accuracy, control costs and protect data is the same across all our AI work. See how we measure AI before it ships
Foundation before agents
An agent is only as safe as what it stands on. Before we give an agent any autonomy, six things must be true.
- The data it needs is clean and reachable.
- Its job is narrow and clearly defined.
- Its permissions cover that job and nothing more.
- A person approves anything that can't be undone.
- Every action is logged.
- Accuracy and cost are measured, not assumed.
Agent, or simpler automation?
When you need an agent
- The work has several steps that depend on each other
- Each case needs reading, checking or judgment
- Inputs vary too much for fixed rules
When simpler automation is better
- The steps and rules never change
- Another system can send the data in a structured file
- The volume is too small to justify the build
We'll tell you which one your workflow needs, even when the answer is not an agent.
Built by engineers who've run agents in production
- Integriti result
An extraction agent runs in production inside a workers' compensation claims platform: it checks its own output against the page, retries within limits and sends uncertain records to a person. How the document processing works
- Team experience
Our AI engineers have built multi-agent systems that coordinate drafting agents, review their work against a rubric and run agentic payment workflows.
Start with one workflow
A short pilot on one real workflow: we build the agents, keep a person in the loop and measure accuracy, time saved and cost per run before anything goes into production. Fixed scope and fixed fee, agreed before we start and credited toward the full build.
AI agent development FAQ
What is an AI agent?
Software that uses AI models to complete a task in steps, such as reading, checking, drafting and routing, using your systems and tools, rather than answering a single question.
How is an agent different from a chatbot?
A chatbot answers people. An agent does a job in your systems, and hands off to a person when it's unsure or when an action needs approval.
Is it safe to let agents act on their own?
Only within tight permissions, with a person approving anything that can't be undone and a log of every action. That's how we build them.
Can agents work with our existing systems?
Yes, through your APIs and databases, with access limited to what each agent needs for its job.
What does it cost to run an agent?
Every run is logged with its cost from day one, and simple steps are routed to lower-cost models, so you can compare running costs with the manual work they replace.
How do we start?
With one real workflow. A short pilot measures accuracy, time saved and cost per run before anything goes into production. It has a fixed scope and fixed fee, credited toward the full build.
Tell us the workflow you'd hand to an agent
In a 30-minute call with our CEO or CTO, we'll look at the workflow you have in mind and tell you honestly whether it needs agents, simpler automation or neither.
- Whether the workflow suits an agent
- Where people should stay in the loop
- What a pilot would measure