AI Development / AI agents
AI agent development for real business workflows.
As an AI agent development company, we build agents that retrieve data across your systems, reason over it, and act, with a human approval checkpoint before anything high-stakes.
Astra Short on time? Ask about this page in plain words.
Sound familiar? Four problems we are usually called in to fix.
Your team spends the day on lookups
People fetch data from one system to update another, all day, every day.
- Agents that retrieve across CRM, ERP and tickets
- Actions taken through each system’s API
- Every step logged
The pilot agent cannot be trusted with real actions
No permissions, no approval step, no audit trail, so it never goes live.
- Scoped, permissioned tools
- A person approves high-stakes steps
- Full audit trail
Automation breaks on the first exception
Scripts fail when an input does not match the template, and the work lands back on people.
- Reasoning over messy inputs
- Exceptions routed to a person
- Checks against source systems
Agents are live but nobody can measure them
Accuracy, cost per task and failure points are a guess.
- Evals on real cases
- Observability on every run
- Cost and accuracy tracked
An agent should do the work, not just talk about it.
Let’s talkAI agent development services. From choosing the right workflow to running agents safely in production.
01 · Design
Design what the agent may do.
The workflow, the tools, the permissions and the points where a person decides.
- AI agent strategy
- Agent architecture & tool design
- Human approval checkpoints
02 · Build
Build it into your systems.
Agents connected to the tools your team already uses, tested against real cases.
- Custom AI agent development
- System integration
- Multi-agent orchestration
- Evals & verification
03 · Run
Run it in production.
Every action logged, measured and supported once the agent is live.
- Observability & audit trails
- Deployment & support
- Dedicated team
How it works. A replay of the workflow from a delivered engagement.
- 1Update recordCRM · 1 record · 0.8s
- 2Send notificationEmail · 2 recipients · 1.1s
- 3Release paymentPayments · over approval limit · 2.4s
Resolve this request across our systems. Check with me before any payment.
New task received
Retrieving data across systemsData retrieved across systems
Reasoning and proposing actionsActions proposed
Executing low-risk actionsLow-risk actions executed
Checking action riskHigh-stakes action sent for approval
Waiting for human approvalApproved, action executed
Running evals and logging traceEvals passed, trace logged
AI agent solutions we have shipped. Each result comes from a delivered engagement.
AI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effort AI · HealthcareMulti-agent voice AI
Live voice agents for scheduling, verification and follow-up calls, escalating to a person when needed.
~50% fewer manual handling cases AI · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · TravelCustomer support assistant
A support assistant grounded in live inventory data, available around the clock.
~35% fewer repeat contacts AI · LegalResearch & review assistant
Citation-grounded answers, each one traced back to its source document.
~50% faster review time AI · Workflow automationWorkflow automation platform
AI extraction feeding a deterministic rules engine, with consistency checks built in.
~70% faster document prepWhy teams put agents to work. The gains come from removing hand-offs, not from replacing judgement.
Less repetitive work
Agents take on the lookups, cross-checks and routine updates people currently do by hand.
Review where it counts
People review exceptions and high-stakes actions instead of every single item.
Around-the-clock coverage
Agents keep working outside business hours, as our travel support assistant does.
Connected systems
One agent can work across CRM, ERP, ticketing and documents instead of people switching tabs.
Auditable by default
Every step is logged, so any decision can be traced and reviewed later.
Faster turnaround
Work that used to wait in a queue moves as soon as the data is there.
Have a workflow ready for an agent?
Industries we have delivered AI in. Each one links to the engagement behind it.
From workflow to production agent. Four stages, with a working version reviewed at each one.
-
Discovery
Map the workflow, the systems involved, and which actions carry risk.
Discovery notes
- The workflow to automate
- Systems and data involved
- Actions that need approval
-
Scope & architecture
Define the agent's tools, permissions, approval checkpoints and delivery milestones.
Agent design
- Tools and permissions
- Approval checkpoints
- Delivery milestones
-
Build
Build the agent against a working version, with evals reviewed at each milestone.
Working agent
- Agent built against real data
- Evals on representative cases
- Review at each milestone
-
Ship & support
Roll out to production with monitoring, audit trails and a defined support window.
Production rollout
- Production deployment
- Monitoring and traces
- Defined support window
Why Avinya for AI agents. Governance is part of the build, not an add-on.
Human approval built in
Approval checkpoints are part of the workflow from day one, rather than bolted on afterwards.
Every action logged
Agent actions are logged and auditable, so nothing happens in a black box.
Verified against source
Agents verify their own output against source systems before it counts as final.
Autonomy where it is earned
Agents act on their own for low-risk steps and defer to people on high-stakes ones.
Built for real operations
Designed to run against real production systems, not a sandboxed demo.
AI and Web3 under one team
One studio for agents, data pipelines and, where it helps, on-chain infrastructure.
Results from our AI agent work. Each number links to the engagement it came from.
How to start. Three ways in, depending on how well defined the problem is.
AI readiness assessment
We review the workflow, data, systems and controls. You get a written report with the first use case and a roadmap.
4–6 weeksAI proof of concept
One AI use case tested against your real data, with evals, so you know whether it pays off before a full build.
30 minutesTechnical call
Talk the problem through with an engineer: the systems involved, the constraints and a realistic approach.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
AI platform for an insurance broker
“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
Guides on this topic. Plain answers to the questions buyers ask first.
AI Agent Development Cost: What Drives the Price (2026)
What drives the price of an AI agent: scope, complexity, iterations and retainer.
Read the guide → GuideAgentic Commerce: How AI Agents Shop and Pay (2026)
How AI agents search, decide and pay on behalf of shoppers, and what to build first.
Read the guide → GuideAI Customer Churn Research: Why Follow-Ups Beat Surveys (2026)
How a conversational agent finds out why customers left, with the quotes behind every theme.
Read the guide →AI agent development FAQs.
What is an AI agent, and how is it different from a chatbot?
A chatbot answers questions. An agent can also take actions: it retrieves data, reasons over it, and completes steps in your systems. We design agents to act on low-risk steps and to ask for human approval on high-stakes ones.
How do you keep AI agents safe?
Every tool an agent can use is scoped and permissioned, high-stakes actions route through a person before they execute, outputs are verified against source systems, and every action is logged so it can be audited.
Which systems can your agents connect to?
Any system with an API or a structured export, typically CRM, ERP, ticketing and knowledge bases. We confirm the exact integration points during discovery.
Which AI models do you use?
We evaluate models against your real workflow and choose per task. The architecture is designed so models can change later without rebuilding the product.
How long does it take to build an AI agent?
It depends on scope and integrations. We agree milestones up front and review a working version at each one.
How do you measure whether an agent works?
With evals on representative cases before launch, and with traces and monitoring once it runs in production.
How do you choose an AI agent development company?
Ask which actions the agent can take on its own, which need approval and how every action is logged. As an AI agent development company we set those limits with you before build, using the NIST AI Risk Management Framework as a checklist for risk.
How much does AI agent development cost?
AI agent development with us starts from USD 10,000 and rises as the scope grows, depending on the scope of work, complexity, iterations and any retainer. Our guide to AI agent development cost breaks down what drives the price and how to start small.
Free tool
How ready is your business for AI?
Twenty questions, about four minutes. You get a score across five areas and what to fix first.