AI agent development
AI agents designed to do a job inside your business systems
An AI agent becomes useful when it can gather context, reason over a defined task, use approved tools and move work forward within clear boundaries.
AI Creative develops agents for operational and knowledge workflows where a chatbot alone is not enough. The agent can retrieve information, call APIs, prepare an action, request review and update connected systems after approval.
Example interface
Agent workspace / run trace
- IntakeRequest receivedCustomer reports a failed delivery against two open orders.
- Record lookupRetrieve contextAccount, order history and the service policy applying to this account tier.
- Policy checkCheck policyDeterministic check confirms the account is inside the replacement window.
- Draft actionPrepare actionProposes a replacement order and a response to the customer, with the clause it relied on.
- EscalationStop and askOrder value is above the automatic threshold, so the run pauses for approval.
- Update recordApply decisionAfter approval the agent writes the order and logs the tool calls behind it.
Step 1 of 6
Capability model
What makes an agent different from a chatbot
A chatbot primarily exchanges messages. An agent can also use tools and execute a sequence of steps toward an objective.
For example, an agent might read a customer request, retrieve account context, check policy, draft a recommended action, route it for approval and then update the CRM after a person approves the decision.
That capability is useful only when permissions, tool access and stopping conditions are defined carefully. If the job is conversation, start with chatbot development →
Stopping conditions are a design decision
Tool access
Give the agent the minimum tools it needs
Agents may need access to search, databases, APIs, calculators, document systems or internal functions.
Tool access should be explicit. The agent should not have broad write access simply because it is technically convenient. Sensitive actions can require human confirmation or additional validation.
Example system view
Retrieve
Reason
Act
Context
Ground reasoning in business context
An agent may need customer history, policies, product information, current system state or prior actions to make a useful recommendation.
Retrieval should respect user permissions and data ownership. Context should also be limited to what is relevant enough to support the task, rather than passing large volumes of unfiltered data into every request. See how the retrieval layer is built →
Governance
Separate agent autonomy by action type
An agent does not need one global level of autonomy. Reading data, drafting a recommendation, creating a low-risk task and changing a financial record can each have different permission rules.
We define those actions individually and require stronger controls as consequence increases. That makes the system easier to expand. A workflow can begin with read-only research and human-approved actions, then automate selected steps later if evaluation data shows the behavior is reliable enough.
Example control model
Read and research
Permitted
- Retrieve records and documents under the user's permissions
- Assemble context for a person
Requires a person
- Any change to a business record
Review
Human review is part of the architecture
High-impact actions should have a visible review path.
The reviewer should be able to approve, revise or escalate with the source context and proposed action in one place. The agent can then continue the workflow based on that decision.
Evaluation
Evaluate the agent as a system
Agent quality is not just answer quality. Testing should cover normal cases, edge cases and the situations where the correct behavior is to stop and ask for help.
| Measure | What it tells you |
|---|---|
| Tool selection | Did the agent choose the right tool for the step, or reach for a write when a read was enough? |
| Completion rate | How often the run reaches a usable outcome without a person restarting it. |
| False actions | Actions taken that should not have been, and what they cost. |
| Escalation rate | How often the agent correctly stops and asks for help. |
| Latency and cost | Time and spend per run, measured against the manual baseline. |
| Recovery | Behaviour when information is incomplete, conflicting or unavailable. |
Examples
Examples of agent workflows
Each of these has a defined job, a system of record behind it and a person accountable for the outcome.
Service-request triage
Read the request, gather account context and prepare the resolution for a person to confirm.
Vendor or contract review
Assemble the clauses, obligations and prior positions a reviewer needs in one place.
Account research
Compile history, open items and relevant activity ahead of a sales conversation.
Policy and procedure assistance
Answer internal questions from approved procedure content with the source attached.
Exception investigation
Trace an operational exception across systems and propose the corrective step.
Cross-system coordination
Move a case forward where the work touches several applications.
FAQ
Frequently asked questions
Yes for actions that are low-risk and explicitly permitted. Higher-consequence actions can require approval before execution.
Yes through approved APIs, tools or application functions, subject to permissions and integration access.
Yes. The architecture can route tasks to different models where that improves cost, quality or resilience.
By logging requests, tool calls, decisions, review outcomes, errors and evaluation metrics appropriate to the workflow.
Build an agent around a real job, not a demo conversation
Describe the work you would hand to a capable new team member. We will define the tools, the boundaries and the review path that job actually needs.