What it means for your business
An AI agent is a software component that uses a language model to interpret information and request access to permitted tools. Within a business workflow, it can classify a request, retrieve records through an API, prepare a decision and submit an action for approval. Its value is measured by completed tasks, not chat responses.
When it makes sense
When teams spend time finding information, triaging requests and moving data between systems. An AI agent is useful when a task requires interpreting unstructured text and selecting a next step within a defined process. If clear rules describe every step, conventional automation may be simpler and more reliable.
Example workflows
Illustrative workflows. The actual scope and system access are agreed for each project.
Customer support
Request → classification → knowledge retrieval → suggested response → operator review. An agent can prepare a ticket update while retaining links to the materials used.
Document processing
Document → field extraction → format and completeness checks → reference data validation → exception queue. Ambiguous values go to a person rather than being treated as verified facts.
Internal operations
Employee request → access check → retrieval from business systems → proposed action. Updating a record or starting an operation follows agreed approval rules.
Choosing an approach
| Approach | How it works | Best suited to |
|---|---|---|
| Chatbot | Answers questions and helps users find information. | When the goal is an answer or navigation. |
| Rule-based automation | Executes a predefined sequence. | For stable workflows with formal conditions. |
| AI agent | Interprets inputs and selects permitted actions. | For variable tasks involving text, tools and exceptions. |
Controls and reliability
Text in an email, document or external page must not become an instruction with permission to change the system. We separate untrusted content from control rules, scope available tools and validate their inputs. We check user permissions, set time and call limits, and design retries to avoid duplicate operations where the integration supports it. Failures and incomplete actions need to be visible to an operator.
The first pilot
We start with one workflow: who triggers it, which data it needs and what counts as completion. We assign steps to deterministic code, the model or a person. Agents receive only the required tools and permissions; changes to important records can require staff approval.
Choose one workflow with an accountable owner and historical examples. Measure the manual baseline, then evaluate the agent on a separate set that includes errors and unusual cases. Start with recommendations or drafts and enable agreed actions after review. Decisions about expansion should follow evidence, not the impression made by a demo.
Scope of work
- Multi-step workflow automation
- Agents for support and operations
- CRM, ERP and API integrations
- Human approval and monitoring
What you receive
An implemented workflow, agreed system integrations, evaluation examples, action logs and an operating guide. The pilot includes acceptance criteria and observations on quality, processing time and API costs. Expansion follows evaluation under realistic conditions.
What we measure
- Task completion
- Share of scenarios accepted against predefined criteria.
- Human involvement
- Corrections, approvals and handoffs to manual processing.
- Time and cost
- End-to-end handling time and cost per successfully completed task, including retries.
- Incorrect actions
- Permission violations, wrong changes and incidents requiring recovery.
Questions and answers
Where should an AI agent implementation start?
With one repeatable workflow, an outcome owner and representative examples. Define data sources, permitted actions and acceptance criteria before development.
Can an agent connect to our CRM or ERP?
Yes, where a suitable API or another agreed interface is available. We review methods, permissions, rate limits and test environments before estimating the integration.
Can an agent update records on its own?
It can perform explicitly permitted operations. Sensitive changes can require human review; a pilot can be limited to read access and drafts.
How do we assess automation ROI?
Compare the process cost before and after implementation, including integration, API usage, maintenance, human checks and error correction. Time saved does not automatically translate into lower expenditure.
How much does a custom AI agent cost?
Cost depends on workflow complexity, integrations, data quality and control requirements. Discovery allows a pilot and production implementation to be estimated separately.
Prepare for the first conversation
Describe the workflow, current systems and expected outcome. The first discussion identifies discovery questions and the boundaries of a possible pilot.
Open the project brief →