AI Solutions
Applied AI with a business case, not a science project.
AI & Automation
Agents that finish the job
A chatbot answers. An agent acts — reads the ticket, checks the system, drafts the response, updates the record, and escalates when it should not proceed alone.
- Engagement
- Workflow-scoped build then expansion
- Typical timeline
- 6–12 weeks per workflow
- Delivered across
- All 50 US states
Overview
The step change in AI over the last two years is not better prose. It is reliable tool use: a model that can decide which system to query, in what order, and what to do with the result. That turns AI from a writing aid into an operational participant, capable of clearing the routine middle of a workflow while people handle the exceptions.
It also raises the stakes. An agent with write access to your CRM can do real damage, so the engineering discipline matters more than the prompt. We build agents with narrowly scoped tools, explicit permission boundaries, deterministic guardrails around every side effect, and a full trace of what the agent did and why. Approval gates sit wherever an action is expensive to reverse.
We start small on purpose. One workflow, clear success criteria, a human reviewing output until the numbers justify loosening the reins. Agents earn autonomy the same way a new hire does — by demonstrating reliability on work you can check.
- Share of routine cases handled without a human
- 60–80%Share of routine cases handled without a human
- Coverage without adding headcount
- 24/7Coverage without adding headcount
- Trace of every action and decision
- FullTrace of every action and decision
Business benefits
What ai agents changes for you
The reasons this work earns its budget, stated as outcomes rather than features.
Real tool access
Agents that query your database, call your APIs, read your documents and update your systems — with scoped credentials and per-tool permissions.
Approval gates where they matter
Configurable checkpoints before any irreversible action: refunds, external emails, record deletion, financial commitments.
Reliable multi-step execution
State machines and durable workflows underneath the model, so a failure halfway through resumes rather than restarts or half-completes.
Observable reasoning
Every run traced end to end — inputs, tool calls, intermediate reasoning, outputs — so debugging and audit are straightforward.
Graceful escalation
The agent knows what it does not know. Low confidence, unusual input or out-of-scope requests route to a person with context attached.
Continuously evaluated
Regression suites run against every prompt, model or tool change, so quality improvements are verified rather than hoped for.
Problems solved
If any of this sounds familiar
These are the situations clients describe in the first conversation, and what we do about each.
“Support agents spend their day on the same twenty questions.”
An agent handles the repeatable tier with access to order, account and policy data, escalating anything unusual with a full summary.
“Sales reps lose hours to research and CRM admin.”
A research agent that assembles account briefings, drafts follow-ups and logs activity automatically, leaving reps to sell.
“Document intake requires manual reading and rekeying.”
An extraction agent that reads incoming documents, validates against business rules, and files exceptions for human review.
“We tried an agent and it did something unexpected.”
Almost always missing constraints. We add deterministic guardrails, restrict tool scope, and introduce approval gates before irreversible actions.
Our process
How we deliver it
Each stage has a defined output, so you always know what you are getting and when.
- 01
Workflow selection
Identify a bounded, high-volume workflow with checkable output and a clear definition of success and failure.
- 02
Tool and permission design
Define the exact tools the agent may use, the data it may see, and the boundary at which it must ask a human.
- 03
Build and evaluate
Agent implementation with a labeled test set, measuring task completion, accuracy and escalation rate before anyone relies on it.
- 04
Supervised rollout
Human review of every output initially, then sampled review as measured reliability supports reducing oversight.
- 05
Expand
Add tools, widen scope or take on the next workflow, using the same evaluation discipline each time.
Technologies used
The tools behind the work
Chosen for maintainability and hiring depth rather than novelty. We will justify any choice on request, and we avoid technology that makes you dependent on us.
- Claude
- OpenAI
- LangGraph
- Temporal
- Model Context Protocol
- Python
- TypeScript
- PostgreSQL
- Redis
- LangSmith
Industries served
Where this work lands most often
Sector context changes what good looks like. These are the industries where we have delivered this service repeatedly.
Why Mova
What working with us on ai agents is like
The same commitments apply to every engagement, regardless of size or service.
A defined first step
We scope a fixed-price starting point so you can evaluate us on ai agents before committing to a program.
Senior people, named
The team you meet is the team that delivers. You will know exactly who is accountable.
You own the output
Code, accounts, files and documentation are yours from day one, with no lock-in of any kind.
Measured, then reported
We baseline before starting and report against it honestly — including the months that fall short.
Questions
AI Agents: common questions
The questions we are asked most about ai agents, answered directly.
A chatbot produces text. An agent takes actions — calling systems, making decisions across multiple steps, and completing a task end to end. The engineering difference is substantial: tool design, permissions, state management and failure handling all become central.
Keep exploring
Related services
Chatbot Development
Conversational support that resolves instead of deflecting.
AI Automation
Automating the work that was too messy to automate before.
Workflow Automation
Connecting your tools so work moves without anyone pushing it.
API Development
Interfaces other teams can build on without asking you questions.
Next step
Ready to talk about ai agents?
Thirty minutes with someone who has delivered this work. We will tell you what it would take, roughly what it would cost, and whether Mova is the right fit.
No pitch deck. A 30-minute conversation about what you are trying to achieve.