The agent can see a duplicate charge and prepare the refund. The business still needs a rule for the amount it may execute, when approval is required, and how the refund is reconciled with the order and finance systems.
AI Agent Development
AI agent development for workflows where AI is trusted to take action.
The engineering problem changes when an AI assistant moves from answering a question to changing a customer record, scheduling an appointment, issuing a refund, sending a message, or triggering another system. Dream Beyond designs the surrounding software that defines what the agent may see, what it may do, when a person must approve the action, and what evidence remains afterward.
Agentic systems
Design the authority around the agent and the actions it may take.
A production agent sits inside an operating system of context, tools, permissions, approvals, evaluation, and audit.
Agent
bounded authority
Context
Tools
Identity
Approval
Evaluation
Where the risk becomes real
A convincing demo can hide the decisions that production forces the business to make.
A demo usually proves that the model can understand a request and choose a plausible next step. A production workflow must also survive incomplete data, permission boundaries, unavailable tools, duplicate requests, ambiguous policy, changing integrations, low confidence, human escalation, and the possibility that an apparently reasonable action is still the wrong action to execute.
The agent can detect an exception and determine the likely next step. Production design defines which records it may change, what evidence it must attach, and when the exception leaves the agent's authority.
The agent can find an available appointment and collect the information needed to proceed. The workflow must still define identity, system-of-record ownership, confirmation, cancellation, and the point where staff take over.
The agent can retrieve policy and draft a recommendation. Evaluation must show whether it used the right sources, applied the right rule, and escalated when the evidence was incomplete.
Capability ≠ authority
An agent can know how to take an action before the business has decided it should be allowed to.
Production design separates what the model can infer from what the software is authorized to execute. Approval, permission, amount limits, identity, tool scope, and audit history belong in the surrounding system where they can be enforced and changed.
The consequence of a wrong answer and the consequence of a wrong action are different engineering problems. Authority should expand only when the evidence supports it.
Example request
“Refund the duplicate charge on order 4417.”
Read
Retrieve the order, policy, and payment history
Observe onlyRecommend
Determine whether the duplicate-charge rule appears to apply
Human decidesPrepare
Prepare the refund amount and the record update
Action is stagedApprove gate
Require fresh approval when the amount or consequence crosses a boundary
Authority is explicitExecute
Call the permitted tool and record the result
Bounded actionEvery consequential step should leave evidence: context used, decision made, approval received, tool called, result returned, and downstream state changed.
What AI agent development services should include
The agent is one component inside a production operating system.
Production readiness depends on the surrounding controls and integrations as much as the model behavior. These responsibilities should be designed together because each one changes what the business can safely delegate.
One bounded responsibility
Define the job the agent owns, the outcome that counts as complete, the conditions that require escalation, and the work that remains human-owned.
Context and data access
Specify which records, documents, systems, and retrieval sources the agent may use, how fresh they must be, and what evidence must accompany a decision.
Tools and integrations
Connect APIs, applications, databases, communication channels, and internal services through explicit contracts with bounded tool access.
Identity and permissions
Give the agent a recognizable identity and least-privilege permissions so its actions can be attributed, limited, reviewed, and revoked independently.
Approval boundaries
Define which actions are read-only, advisory, staged for approval, autonomously allowed within limits, or prohibited without a person making the decision.
Evaluation and observability
Measure task completion, groundedness, tool-call accuracy, escalation behavior, latency, cost, policy adherence, and the business outcome the agent exists to improve.
Audit evidence
Record relevant context, model decisions, tool calls, approvals, outputs, and downstream effects so an important action can be reconstructed after it happens.
Failure and recovery
Design timeouts, unavailable tools, conflicting data, low confidence, partial completion, duplicate actions, rollback, and human takeover as normal production paths.
Self-diagnosis
Before funding the agent, answer the authority questions.
These questions expose the parts of the implementation that usually appear after a prototype has already created expectations. Answering them early makes the first production boundary smaller and easier to defend.
Use the Agent Authority FrameworkCan we state the agent's responsibility in one sentence without describing a model or interface?
Which business systems can the agent read, and which can it change?
Which actions could create financial, customer, security, compliance, or irreversible consequences?
Where is human approval required, and is that approval enforced by software outside the prompt?
Can we reconstruct the evidence, tool calls, approvals, and downstream changes behind an important action?
What happens when a tool times out, data conflicts, confidence is low, or only part of the workflow completes?
Which evaluation would stop a bad agent change from reaching production?
Can we reduce or revoke the agent's authority without redesigning the entire workflow?
A controlled implementation path
Start with one responsibility and widen authority only when the evidence earns it.
- 01
Map the human workflow, including decisions, systems, data, exceptions, and the consequence of a wrong action.
- 02
Choose one bounded agent responsibility with a measurable definition of done and an explicit human escalation path.
- 03
Design context, tools, identity, permissions, approval, audit, and recovery around that responsibility.
- 04
Build an evaluation set from normal, difficult, ambiguous, and failure cases before production authority expands.
- 05
Deploy behind the appropriate human gate, observe real behavior, and compare agent actions with downstream business state.
- 06
Increase or reduce authority deliberately as evidence changes; keep the control boundary independent from the prompt and model provider.
Dream Beyond research & proof
The controls behind the implementation are part of the product thinking.
AI Agent Authority
A five-level model for separating agent capability from the authority delegated to act inside the business.
Read the researchAI Software Assurance
How evaluation, observability, evidence, and operational ownership continue after the first demonstration succeeds.
Read the researchAI Tutor capability exploration
See an internal AI build in the context of the user responsibility, information flow, and software surrounding the model.
View the capability explorationDiagnose first
AI Opportunity Assessment
Rank candidate workflows by value, readiness, reversibility, evidence, and consequence before selecting the first implementation.
See the assessmentImplement a bounded responsibility
AI Agent Implementation
Build the agent workflow, integrations, permissions, human gates, evaluation, observability, and deployment around a clearly defined business responsibility.
See the implementation offerQuestions buyers usually ask
Clarify the operating responsibility before choosing the agent architecture.
What makes an AI agent different from a chatbot?
A chatbot primarily returns information. An AI agent can maintain workflow state, use tools, access business systems, and take actions. Those additional responsibilities require identity, permissions, approval boundaries, evaluation, audit evidence, and recovery behavior around the model.
What should AI agent development services include?
A production engagement should define the agent's responsibility, data and tool access, identity, permissions, integrations, approval rules, evaluation, observability, audit history, exception handling, deployment, and ongoing ownership. The exact architecture follows the consequence of the work being delegated.
How much autonomy should an AI agent have?
Authority should match the consequence of failure and the quality of available evidence. Low-consequence bounded actions can usually earn autonomy earlier. Financial, security-sensitive, customer-facing, regulated, or difficult-to-reverse actions need stronger controls and human decision points.
How do you evaluate an AI agent before production?
Evaluation should represent the real workflow: task completion, source use, tool-call accuracy, policy adherence, escalation, exception handling, latency, cost, and downstream state. A useful evaluation set includes the normal path, difficult cases, and failure cases.
A safer place to start
Choose one workflow where useful autonomy can be measured and controlled.
We can map the workflow, define the authority boundary, identify the required integrations and evidence, and determine whether the next step should be an assessment, a proving implementation, or a larger agent system.