Engineering services · AI agents
AI Agent Development Company
Put artificial intelligence to work in your business.
We design and build AI systems that plug into how your business actually operates — from generative tools that speed up content and analysis, to autonomous agents that carry out multi-step tasks on their own.
Related services: AI Chatbot Development Company in India and LLM Application Development Services.
What this service is
Who it's for, and what it fixes.
An AI agent is software that uses a language model to work through a task in steps: it reads a request, decides what it needs, calls your systems through tools you allow, and either finishes the job or hands it to a person. Done well, it takes on work that needs some judgement, such as triaging requests, preparing a quote or reconciling records. Done badly, it is an unpredictable script with access to your data.
We build agents for clients that are narrow, measurable and supervised. Each agent has a clearly defined job, a short list of tools with the least access that job needs, a test set of real cases it is scored against, and a log of every step it took. People approve anything that matters until the numbers show it can be trusted with more.
Support and operations teams
Requests that need reading, looking things up in two or three systems and deciding the next step.
Sales teams
Preparing proposals, qualifying leads or updating records from emails and calls.
Finance and back office
Matching documents, checking records against rules, and preparing exceptions for a person.
Product companies
Adding an agent to your own product that works on your customers' data safely.
Problems it solves
- Skilled people spend their day on look-up-and-decide tasks
- Requests wait in a queue for someone to triage them
- An AI pilot impressed in a demo but nobody trusts it with real work
- No way to see why an AI tool did what it did
- Worry about what an AI system could access or change
What's included
The deliverables.
What a typical engagement produces. Discovery decides which of these your first release needs.
Use-case selection
Candidate jobs scored for value, risk and how measurable they are, so the first agent is one that can be proven.
Evaluation set
A set of real past cases with the right outcome for each, used to score the agent before and after every change.
The agent
The model, its instructions and its tools (search, look-up, create or update records), built as a service with its own permissions.
Human review
A review screen where people approve, correct or reject the agent's proposed actions, with the corrections fed back into the evaluation set.
Audit and monitoring
Every step, tool call and decision logged; dashboards for accuracy, cost per task and time saved.
Guardrails
Limits on what the agent can do, spend and see, input checks against prompt injection, and safe failure when something unexpected happens.
How an engagement runs
From first call to running software.
Five phases, each ending in something you can see and approve. The detail, and what we need from you at each step, is on how we work with you.
- 01
Discovery 1–2 weeks
We pick one job, collect real examples, define what a good outcome is, and list the systems and the minimum access the agent will need.
- 02
Design 1–2 weeks
The agent's tools, the review flow, the guardrails and the evaluation set, agreed before any model is chosen.
- 03
Build 3–6 weeks
The agent built and scored against the evaluation set every cycle. You see the score, the failures and the cost per task, not just the good examples.
- 04
Launch 1–2 weeks
Released in shadow mode first, proposing actions that people approve. Autonomy grows step by step as the measured accuracy allows.
- 05
Support Ongoing
Monitoring, re-scoring when models or your processes change, and widening the agent's job or adding the next one.
Technology approach
Plain, proven, and yours to keep.
Mostly the stack behind the Varynt products, chosen per project. Read how we build.
- Python
- LangChain
- Java
- Spring AI
- MCP
- PostgreSQL
- pgvector
- Models
- Hosted language models from the major providers, chosen per task on measured quality and cost, behind our own service so the model can be swapped.
- Agent service
- Java with Spring AI, or Python with LangChain, depending on your team and systems, with tools exposed as narrow, typed functions.
- Tools over MCP
- Where it fits, your systems' tools exposed through the Model Context Protocol, so the same tools serve your agent and the assistants your staff use.
- Data and logs
- PostgreSQL for the agent's state, review queue and step logs; vector search such as pgvector when the agent needs your documents.
What you receive
Ownership, documentation, handover.
- The full source code in a repository you own, with its history, from the first week
- A written handover: architecture overview, how to run, build and release it, and the decisions taken and why
- Credentials, keys and accounts created in your name, never ours
- The automated test suites and the build pipeline, so anyone can keep shipping it
- The evaluation set and the scoring scripts, to re-test any change yourself
- A record of what the agent can access and do, and how to change it
Typical timelines
Ranges, not promises.
- Proof on real cases
- 3–4 weeks
- An evaluated prototype on your examples, to decide whether to build for real.
- First agent in use
- 6–12 weeks
- One job, with tools, review flow, monitoring and a staged rollout.
- More agents
- 4–8 weeks each
- Faster once the tools, logging and review flow exist.
Every project is quoted for its own scope after discovery; prices are not published.
Security and quality
The practices we use on our own products.
Varynt runs four products for paying customers. Client work gets the same habits. See trust and security.
- Agents act through their own service accounts with the least access their job needs
- Scored against the evaluation set before every release; no change ships on a good impression
- Inputs from outside (emails, documents, web pages) treated as untrusted, so they cannot redirect the agent
- Access is checked on the server for every request; hiding a button is never the control
- Code review on every change, and automated tests that run before anything is released
- Test suites run in more than one order, so a test that only passes by luck is caught
- Before a release changes a database, it is backed up and the backup is restored and checked
- Secrets live outside the code, and dependencies are kept current
What is the difference between an AI agent and a chatbot?
A chatbot answers in a conversation. An agent works through a task: it calls your systems, makes decisions within limits and completes or hands over the work. Many projects combine both. See our AI chatbot development service for conversational work.
How do you stop an agent from doing something wrong?
By design: a narrow job, tools with the least access needed, limits on actions and spend, human approval for anything that matters, and a log of every step. Autonomy is increased only when measured accuracy supports it.
How do you measure whether an agent works?
Against an evaluation set of real past cases with known right answers. We report accuracy, failure types, cost per task and time saved, before launch and on every change.
Is our data used to train the AI models?
We use model providers' business offerings, whose terms exclude using your data for training, and send only the data each task needs. We review the terms with you during discovery.
Which language model will you use?
The one that scores best on your evaluation set at an acceptable cost and speed. The agent is built so the model can be changed without rewriting it.
Can an agent work with our existing systems?
Yes, through their APIs, or through a small integration service where there is no API. Each connection is a tool with its own permissions.
What does it cost to run an agent?
Running cost is mostly model usage per task, which we measure during the build and report as cost per task, so you can compare it with the time it saves before deciding to roll out.
Related
Services and products that fit alongside.
Other services: Custom software · Mobile apps · Web applications · SaaS products · Cloud and DevOps · MVPs for startups · Custom CRM
Get in touch
Tell us about the job you want an agent to do.
A short call about the task, the systems and the risks. You get a view of whether an agent is right, and how it would be measured.