Give repetitive tasks an assistant with clear responsibilities and limits.
Build AI agents with BrainGlow for scoped business workflows. Define tools, permissions, evaluations and approval steps before moving from pilot to production.
Our approach to AI Agent Development
An AI agent can coordinate a task across tools, but it needs defined responsibilities and a way to recover when something goes wrong. We start with a bounded workflow and explicit permissions.
The build includes representative evaluation cases, tool-call validation and human escalation. Actions with significant consequences can require approval rather than running unattended.
When to choose this service
- Internal research and knowledge retrieval
- Drafting and triaging operational requests
- Assisted multi-step workflows across business tools
Project success criteria
We agree measurable goals and acceptance checks during discovery. Outcomes depend on the scope, starting point and how the solution is used.
Delivery
Agreed acceptance criteria
How we review this
Measurement
Baseline and project goals
How we review this
Handoff
Documented ownership
How we review this
What You Get
- Agent responsibilities and tool permission boundariesAcceptance checkReview “Agent responsibilities and tool permission boundaries” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Validated tool integrations and action schemasAcceptance checkReview “Validated tool integrations and action schemas” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Representative task evaluations and failure casesAcceptance checkReview “Representative task evaluations and failure cases” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Human approval and escalation pathsAcceptance checkReview “Human approval and escalation paths” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Execution logs, usage monitoring and cost controlsAcceptance checkReview “Execution logs, usage monitoring and cost controls” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Pilot rollout and operating runbookAcceptance checkReview “Pilot rollout and operating runbook” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
How We Execute
- 1Select a bounded task and define successful completionReview this stepFor “Select a bounded task and define successful completion”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 2Choose tools, access boundaries and approval pointsReview this stepFor “Choose tools, access boundaries and approval points”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 3Build and evaluate the agent against realistic scenariosReview this stepFor “Build and evaluate the agent against realistic scenarios”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 4Pilot with monitoring and review before widening scopeReview this stepFor “Pilot with monitoring and review before widening scope”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
Next Step
Discuss your project with BrainGlow
Share your goals and current setup. We will give you a clear scope, timeline, and success criteria before build starts.
AI Agent Development: common questions
What is the difference between an agent and an automation?
A fixed automation follows predefined rules. An agent can interpret inputs and choose among permitted steps. We use the simplest approach that fits the task and can be tested.
Can an agent take actions in our business tools?
Yes, when the tools support the required access. Permissions and approval rules are scoped before implementation, and actions are validated and logged.
How do you evaluate an AI agent?
We use representative tasks and failure cases, then check output quality, tool use, completion and cost. Evaluation criteria are agreed for the specific workflow.
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Your next step
Ready to plan your project with BrainGlow?
Get a concrete execution plan and timeline matched to your growth goals.
Choose a workflow with a clear owner and a recoverable failure path
Bring a process example, the tools involved, and the point where work stalls. Start with one trigger and one expected result. Identify which decisions can follow fixed rules, which need interpretation, and which require a person to approve the next action.
- Document access requirements and the system that owns each field.
- Test duplicate inputs, invalid data, and unavailable integrations.
- Agree on a manual fallback, monitoring owner, and a baseline before expanding.
Review related delivered projects to assess fit. Timeline and outcomes depend on scope, data quality, and third-party access; define acceptance criteria during discovery.