AI agents that finish the work, not just the chat.
Agents that plan, call your tools and complete real tasks inside your business systems, with the guardrails enterprises need. We design, build and run production AI agents: grounded in your data, connected to your systems through MCP, and governed by evaluation, human approval and cost controls from the first sprint. You own the code.
Why most agents stall
An impressive demo is not an agent you can trust with real work.
The demo works, production does not
A prototype that shines on curated examples often breaks on real data, real load and real edge cases.
Nobody can say what it will do next
Without evaluation and logging, an agent is a black box. Leadership cannot approve what it cannot inspect.
Costs and actions run unchecked
Agents that loop, over-call models or send something nobody approved create risk faster than they create value.
It is cut off from your systems
An agent that cannot reach your CRM, mail and data can only talk. Real work needs real, secure connections.
The gap between a demo and a dependable agent is engineering.
Guardrails, evaluation, approvals and clean integrations are what turn a clever model into a system your team relies on.
Agents with guardrails
Why many AI agents never earn the right to run.
Here is what usually goes wrong, and how we ship agents that are useful and safe.
Usually
A tool before a task.
A team picks a model first, then hunts for somewhere to use it, so adoption stalls.
Lunova
A measurable target first.
Every agent starts from a business outcome we can measure, not from a model.
Usually
Answers from thin air.
With no grounding in company data, the agent invents policies and facts.
Lunova
Grounded in your data.
RAG over your own documents and a vector database keep answers anchored.
Usually
No limits.
Nothing caps cost, rate or scope, so one bad loop becomes an expensive problem.
Lunova
Cost and rate controls.
Budgets, limits and monitoring are part of the build, not an afterthought.
Usually
Unreviewed actions.
The agent acts on its own with no approval step for sensitive decisions.
Lunova
Human approval where it counts.
People sign off on key decisions before anything leaves the system.
The Journey is a seven-step process from discovery through growth, with clear outcomes at each stage.
How we work
The Journey
From vision to velocity — precision and ownership at every step.
-
DiscoveryDiscovery
Map the task and the data
We define the outcome, the people involved, the systems to reach and the data the agent may use.
A clear use case with a success measure -
IdeationIdeation
Choose where an agent earns its place
We separate what an agent should do from what plain automation should do.
Scope agreed before build spend -
DesignDesign
Design the agent and its limits
Tools, memory, hand-offs, approval points and failure behaviour are designed up front.
An agent design your team can review -
EngineeringEngineering
Build, evaluate and iterate
Working agent builds with evaluation sets, tool calling and RAG, demoed every week.
Measured quality at every checkpoint -
SecuritySecurity
Test the agent like an attacker would
Our independent security team tests for prompt injection, data leakage and excessive permissions.
Findings fixed and retested before launch -
DeploymentDeployment
Launch with monitoring
Zero-downtime rollout with logging, cost tracking, documentation and repository handover.
A controlled, observable go-live -
GrowthGrowth
Improve with real usage
Priority support, quality monitoring and regular reviews as the agent meets real work.
An agent that keeps getting better
How We Build Production AI Agents
Task & Data
What we deliver
- Use-case and success measure
- Data and access review
- Tool and system map
- Risk and approval rules
Agent Architecture
What we deliver
- Tool-calling design
- RAG and vector search plan
- Human-in-the-loop points
- Cost and rate controls
Agent & Integrations
What we deliver
- Agent logic and tools
- MCP and API connections
- Evaluation harness
- Logging and tracing
Test, Deploy, Improve
What we deliver
- Independent security testing
- Zero-downtime deployment
- Monitoring and cost dashboards
- Priority post-launch support
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Proof in production
Built, delivered and still running.
Real systems we built and still run, chosen for what they prove about our AI agent development work.
P05 · AI SaaS
NOBS Marketplace
NOBS Marketplace · Engineering led by our CTO
A marketplace where an AI agent finds, scores and contacts link prospects, with people approving what matters.
Why it matters here → A Smart Campaign agent with RAG, a vector database and tool calling runs prospecting to outreach, with human approval and cost controls.
- AI agent
- RAG & vector DB
- MCP servers
- Stripe
Why Organizations Choose Lunova for AI Agents
Agents already running in production
NOBS Marketplace and Compel run agentic workflows every day, so we build from experience, not theory.
Guardrails built in from the first sprint
Evaluation, human approval, and cost and rate controls are part of scope.
Connected to your systems through MCP
Agents reach CRM, search, mail and analytics through proper, permissioned connections.
Independent security testing before launch
Our cybersecurity team tests prompt injection, data leakage and agent permissions.
100% code ownership
Agent logic, prompts, evaluations and documentation are yours at handover.
Senior engineers, direct access
You speak to the people building the agent, not to a layer of account managers.
FAQ
Frequently Asked Questions – AI Agent Development
Straight answers on what agents can do, how we keep them safe, what they cost to run and who owns them.
Still have questions? Contact us
What is an AI agent, and how is it different from a chatbot?
A chatbot answers. An agent plans, calls tools and completes a task: it can look up a record, draft an email, update your CRM and ask a person to approve before anything is sent.
What can an agent safely do inside our business?
As much as you permit. We give each agent the narrowest set of tools and data it needs, with human approval on sensitive actions and logging on everything it does.
How do you stop an agent from making things up?
We ground it in your own documents and data with RAG and a vector database, evaluate it against test cases, and make it hand off to a person when it does not know.
How do you control cost?
Budgets, rate limits and monitoring are built in, so usage stays visible and capped. We design for the cheapest model that meets the quality bar.
Which models do you work with?
We work across OpenAI, Claude and Llama, and with LangChain and PyTorch. We choose by task, data sensitivity and cost, and keep you free to switch.
Who owns the agent, prompts and code?
You do. Code, prompts, evaluation sets and documentation transfer to you at launch, with no lock-in.
Still have questions? Contact us →
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Speak with an AI engineer, not a sales script.
No commitment required
Get a clear feasibility answer on the first call.
Fixed price after Discovery
Scope, cost and timeline in writing before build.