Stateful orchestration
Track the task through explicit states so interrupted work can be understood and resumed.
- Completion conditions
- Retry and step limits
- Recovery and cancellation
Custom orchestration
AI agent development with explicit authority.
Build a custom agent that coordinates approved tools and knowledge across a defined multi-step business task.
- Permitted actions
- Read approved context, select from a constrained tool set and progress through explicitly allowed workflow states.
- Human approval
- The workflow owner defines which writes, external communications and consequential decisions need confirmation before execution.
- When work cannot continue
- Stop on missing authority, uncertain tool results or exhausted retry limits; transfer the task state and evidence to an operator.
Workflow design
How the AI agent development works
A reference workflow for the supported task. The specific tools, approval rules and operating limits are agreed during discovery.
- 01
Establish the task
Resolve the user identity, requested outcome and permitted data before creating a task state.
Output
A bounded task with a completion condition
- 02
Select the next step
Use approved context to propose a permitted tool call; constrain arguments to the tool contract.
Output
A validated action proposal
- 03
Authorize and execute
Apply action-level rules and required approvals outside the model, then execute with duplicate protection.
Output
A recorded tool result or review request
- 04
Verify and close
Check the actual system result, limit further steps and return evidence or an actionable exception.
Output
A confirmed outcome and traceable task history
Controls across the workflow
- Enforce permissions outside the model
- Validate tool inputs and results
- Limit retries and retain task state
- Log decisions with agreed data retention
Decisions that shape the scope
- When is an agent useful?
- Use an agent when the next step depends on context or unstructured input. Prefer explicit rules for a fixed sequence that does not require model judgment.
- Should several agents collaborate?
- Start with one bounded workflow. Separate agents only when distinct responsibilities and measurable benefits justify added coordination and failure handling.
- How is authority enforced?
- Authorize tool calls in the application layer. A model instruction alone does not grant access or enforce an approval requirement.
Evidence before expansion
Define what better means.
These are proposed evaluation measures, not reported client results. Agree the baseline, sample and acceptance threshold before the pilot, then review the evidence with the workflow owner.
- Task completion
- Compare completed tasks with reviewer-approved expected outcomes, including cases that should stop or escalate.
- Tool and permission correctness
- Inspect tool selection, arguments, write authorization and duplicate-action handling across normal and adversarial inputs.
- Operating envelope
- Measure latency, model and tool cost, retry frequency and operator effort for a representative workload.
Before you commit
Is this the right engagement?
Build a custom agent that coordinates approved tools and knowledge across a defined multi-step business task. A first release should cover a named task with representative examples and an accountable owner.
- What we need from you
- Bring a process map, example requests, tool or API documentation, access roles, failure cases and the person accountable for the result.
- How you accept the work
- Agree representative cases, failure scenarios and acceptance thresholds with the workflow owner. Review task completion, tool and permission correctness, operating envelope before expanding access or supported tasks.
- Scope & alternatives
- This is an agent orchestration engagement. Broader AI product development, new data platforms and ongoing operations are scoped separately. A deterministic workflow remains an option when model-led decisions add no value.
Capabilities
What goes into your AI agent development
Implementation components are selected for the task and confirmed in scope.
Constrained tools
Expose the smallest useful set of operations with validation outside the model.
- Typed tool contracts
- Action-level authorization
- Duplicate-write protection
Release and operations
Make behavior inspectable and configuration changes testable before wider rollout.
- Representative evaluation set
- Versioned configuration
- Operator runbook
Frequently asked questions
AI agent development questions
01What is a custom AI agent?
What is a custom AI agent?
A custom agent is an AI-enabled workflow that can maintain context, retrieve approved information, use tools, and complete constrained multi-step tasks under defined permissions and human oversight.
02How much autonomy should an AI agent have?
How much autonomy should an AI agent have?
Only as much as the use case justifies. We start with the narrowest authority that creates value and increase autonomy only when evaluation, controls, and operational evidence support it.
03Can an agent connect to our internal systems?
Can an agent connect to our internal systems?
Yes. We can expose approved CRM, ERP, ticketing, data, communication, workflow, or internal APIs as constrained tools with authentication, validation, and explicit read or write permissions.
04Do AI agents need long-term memory?
Do AI agents need long-term memory?
Not always. Many workflows only need session state plus retrieval from authoritative systems. Longer-lived memory should be introduced only when it creates value and the privacy, retention, correction, and access implications are understood.
05How do you test an AI agent?
How do you test an AI agent?
We create representative task and failure cases and evaluate outputs, tool selection, tool arguments, permissions, state transitions, escalation, latency, cost, and final workflow outcomes.
06Do you claim the agent is automatically SOC 2, ISO 27001, HIPAA, or GDPR compliant?
Do you claim the agent is automatically SOC 2, ISO 27001, HIPAA, or GDPR compliant?
No. Compliance depends on the organization, deployment, data, contracts, controls, jurisdiction, and full operating environment. We design around identified requirements and avoid treating use of an AI architecture as certification.
Related
Connected services and workflows
Start a conversation
Scope your AI agent development
Share the task, systems and examples of a successful result. We can define the first workflow, required controls and evaluation plan.