Build an AI agent that can do real work without operating unchecked.
We engineer agents around one business workflow, the knowledge they may use, the actions they may take and the evidence required before the result is accepted.
- One workflow first
- Grounded company knowledge
- Permission-scoped actions
- Evals and monitoring
Know. Decide. Act. Verify.
- 01GroundApproved sources and context
- 02ReasonTask rules and decision boundaries
- 03ActScoped tools and permissions
- 04CheckEvals, review and audit trail
A convincing demo is not a production agent.
The hard part begins after the model produces a good answer. A useful agent must find the right context, use tools safely, handle failure, ask for approval and leave enough evidence for a person to trust the outcome.
Without those layers, the agent becomes another chat interface or an uncontrolled automation hidden behind natural language.
An operating workflow with an agent inside it.
The model is one component. We design the full path from trigger to reviewed business result.
Start narrow, prove the loop, then expand authority.
- 01
Choose one workflow
Select work with a stable trigger, clear recipient and visible operating loss.
- 02
Map knowledge and actions
Define what the agent may read, decide, change and escalate.
- 03
Build the controlled loop
Connect models, retrieval, tools, approvals and observability.
- 04
Evaluate in real cases
Test correctness, failure handling and business effect before wider rollout.
Best for repeatable work with a clear owner and destination.
Agents create leverage when the task can be bounded and checked. Ambiguous authority should remain with a person.
Good starting workflows
- Support or knowledge triage with source-linked answers.
- Lead, inquiry or document qualification with human handoff.
- Research, proposal or operations assistance with a defined reviewer.
We do not start with
- A general autonomous employee expected to handle every process.
- Irreversible actions without explicit approval and rollback rules.
- A production launch without representative evaluation cases.
What buyers usually ask before scoping the work.
These answers define the normal starting boundary. The project scope follows the workflow, evidence, systems and risk.
What is the difference between an AI agent and a chatbot?
A chatbot mainly responds. An agent operates inside a workflow, uses approved knowledge and tools, manages task state and produces an outcome that can be checked.
Can the agent update our CRM or internal systems?
Yes, when the integration, permissions, validation and human approval rules are explicit. We start with the narrowest useful action set.
How do you test an AI agent?
We build representative cases, expected behaviors, reject conditions and failure scenarios. Production monitoring then checks drift, errors and escalation behavior.
Can you use our preferred model or cloud?
Usually. Model and infrastructure choices follow the workflow, data boundary, reliability requirement and total operating design.
Bring the workflow, not a request for a generic bot.
Describe the trigger, current process, systems and result you want. We will identify the smallest agent loop worth scoping.
Discuss an AI agent