AI agent development
An AI agent reads a request, picks the next step, uses a tool it is allowed to use and checks the result. It repeats this until the task is done or a person needs to take over. The application around it decides what it can access, which actions need approval and when it stops.
We build the whole system around the model. That means designing the workflow, choosing the model, connecting tools and data, handling state and permissions, and writing evaluations before we deploy the system and monitor it. We can start with one narrow task or improve an agent you already use.
What is an AI agent?
An AI agent is software that uses a language model to decide how to complete a task. A fixed script runs the same steps every time. An agent picks from the tools it has and works out each step from the result of the one before.
When it's a good fit
In an agent, the model decides the next step. This helps most when the outcome of a task is clear but the route to it depends on what the documents say, on intermediate results or on exceptions.
A good fit
People decide the next step by reading emails, documents or free-form requests, the task spans several systems whose results set the order, or a rules engine keeps collecting exceptions. You also need someone to review uncertain cases, and a record or other visible result that shows the work was done correctly.
A chatbot may be enough
If people need answers, summaries or drafts and nothing has to change in another system, an AI assistant or a search interface is simpler.
Answer a policy question · Summarize a document · Draft an email
Ordinary automation may be enough
If the same known steps apply to every case and the rules and inputs are stable, code that follows fixed steps is usually the better option.
Copy a field on submission · Send a scheduled report · Validate a known format
Not a good fit
If the result cannot be checked reliably, or a wrong action would be unacceptable and there is no time to wait for approval, an agent is the wrong choice.
Examples of agent tasks
Each agent has one specific task. A goal for a whole department is too vague to design an agent around or to test.
Support case investigation
Read the request and look up the account and its recent activity. Search approved documentation for a resolution, or send exceptions to the team that handles them.
CRM, ticketing, product database, knowledge base
Document intake
Classify an incoming document, extract required fields, check them against company rules, request missing information and prepare the record for review.
Email, document storage, OCR, ERP or case system
Internal service requests
Work out what an employee is asking for and collect any missing details. Once the request is checked against policy, open the right ticket and report its status in the same conversation.
Chat, identity, HR, IT service management
Research and reporting
Split a question into searches across approved sources, keeping a reference for each finding, and write up the comparison in a set report format.
Search, internal documents, databases, reporting tools
Operations follow-up
Find delayed or incomplete work, check its current state in connected systems, contact the person responsible and update the record when they reply.
ERP, project management, email, messaging
Order and request handling
Check an order, return or change request against stock, policy and the customer's history. The agent prepares the update, and a person approves it before it is applied.
Order management, ERP, CRM, email
What we build
We start by writing the task down with a start and an end you can observe. An instruction such as "handle customer operations" can't be tested, and nobody can say which permissions it should have. The rest of the system is built on that definition.
Task definition
What starts the task, who can start it, what inputs it needs and which actions it may take. It also records what counts as done, when a person takes over and which limits stop it.
Tools and integrations
Each API, database, document store, browser-based application, MCP server or internal function the agent uses becomes a tool with one narrow job.
Instructions and state
The application holds the task rules and the history that matters. It also tracks intermediate results, retry limits and where the task currently stands.
Model and routing
Each step can use a different model. Simple classification does not need the model used for planning or document analysis.
User interface
The agent can sit in a chat window or an internal application, or work from an inbox or a background queue. It can also run inside a product you already have.
How much can the agent do on its own?
Autonomy is set separately for each action. Reading a product catalog and issuing a refund should not have the same approval rule. Whatever the level, each tool checks the user's identity and permissions and exposes only the narrow operation it needs. The agent never gets a general database connection. Text in emails, documents and tool outputs is treated as data and cannot grant new permissions. Every tool call is logged.
| Level | Agent behavior | Human role |
|---|---|---|
| Read and explain | Reads approved sources and returns information without changing another system. | Checks the answer when needed. |
| Prepare | Drafts a message, form, decision or system update but does not submit it. | Reviews and submits the action. |
| Act after approval | Plans and prepares an action, then waits before a defined high-impact step. | Approves, edits or rejects. |
| Act alone within limits | Completes selected low-risk actions under value, scope and retry limits. | Reviews logs and handles the cases the agent hands over. |
How an agent is tested
A few convincing conversations say little about whether an agent works. Tests need real examples from the task and its difficult edge cases. They also need permission boundaries, tool failures and cases where the right outcome is handing the task to a person.
We turn these into a repeatable evaluation suite. It checks whether the agent completes the task and uses its tools correctly, whether its facts trace back to allowed records and whether it stops or hands over at the defined boundary. It also tracks cost per completed task and how consistent the results are across runs. We run the suite again whenever the instructions, tools, model or connected systems change, and add new cases from production traces.
What you receive
You receive a deployable application for the agreed task. A prompt file or a demo that only works on hand-picked examples would not be enough to run it in production.
- Source code and deployment configuration
- Tool definitions and connected system integrations
- Task instructions, state handling and stopping rules
- Authentication, permissions and approval flows
- Repeatable evaluation cases and test results
- Logging, monitoring and operating documentation
- User or administrator interface, if in scope
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot answers questions or drafts text. An AI agent also does part of the work. It decides the next step, uses your tools and keeps going until the task is done or a person has to take over.
Does an AI agent need access to our company data?
Not always, but most agents used in business need some. They only see what they are allowed to see, under the same permissions as your existing systems.
Can an AI agent work with our existing software?
Usually, yes. Most software can be connected cleanly. When it can't, there are workarounds, but they need more testing and maintenance.
Do we need a multi-agent system?
Rarely at the start. One agent with a few clear tools is easier to test and run. Splitting the work can make sense later, when parts of the task need different rules or owners.
Can an AI agent run on our own infrastructure?
Most of the time, yes. It can run on your own servers or in a private cloud. Some setups keep sensitive steps inside and use an outside model only for tasks you approve.
Which AI model should our agent use?
We test candidate models on your task and choose the one that does it well at an acceptable speed, privacy level and cost. The model can be replaced later without rebuilding the system.
How long does AI agent development take?
That depends mostly on scope. One well-defined task is quicker to build than an agent that spans several systems and teams. We can estimate once we have seen the task and the systems involved.
Can you improve an AI agent we already use?
Yes. We look at how it behaves in use, where it fails and what it costs, then suggest changes or make them ourselves.
Technical references
The approach on this page follows published guidance from model providers and NIST on how agents are built, evaluated, supervised and secured.
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