Before You Build an AI Agent, Define Five Boundaries
Agentic automation changes the governance question from 'what did the model say?' to 'what was the system authorized to do?' This article defines the boundaries before development starts. A useful AI agent may retrieve information, call tools, draft, route, update records or recommend actions. That capability is valuable only when the firm can state the operating authority clearly enough for leadership, users and developers to understand the same design. Why agentic readiness begins with authority, not a model.
The outcome: an Agent Boundary Card before development starts
Before selecting a model or building an integration, leadership should be able to summarize the proposed agent on one page. A practical Agent Boundary Card records five things: purpose, data access, permitted actions, human authority and exception behavior. If those boundaries cannot be stated clearly, development is premature. The card becomes a shared reference for legal operations, IT, security, users and vendors. It also gives the firm a baseline for later change:
when the agent's purpose or authority expands, the change is visible rather than hidden inside a configuration update.
The pain: technical capability can outrun operating permission
Teams often begin with what a platform can do rather than what the workflow needs. A demo can quickly expand from 'summarize this folder' to 'monitor the folder, create a draft, update the matter record and send a notification.' Each new action changes the risk and evidence profile. The problem is not autonomy by itself. It is unexamined authority.
When permissions grow informally, users may not know which actions require approval, which systems are authoritative, or what happens when the agent encounters missing or conflicting information.
Boundary 1 and 2: purpose and data
Define the job and the information envelope
Write the purpose as a narrow business outcome: triage intake for human review, classify a defined document set, prepare a status draft, or retrieve approved internal knowledge. Then define the information envelope. Which repositories may the agent read? Is matter data involved? Can it access email, document management, billing, calendars or external websites? What must stay out of scope? A boundary should be specific enough that a user can recognize when the agent is being asked to do something different. This makes later testing more meaningful because normal and prohibited cases are easier to construct.
Boundary 3: separate reading, drafting and acting
Permissions should expand deliberately
Reading an approved source is different from changing it. Drafting an email is different from sending it. Recommending a matter code is different from updating the system of record. Treat those as separate capabilities. An early agent may be allowed to read designated sources and prepare a draft while every consequential action remains human-approved. If the firm later expands authority, do it as a deliberate change with new testing and evidence. This staged approach can improve adoption because users learn to trust a bounded system before it receives broader permissions.
Boundary 4: define human authority
The human needs a decision, not a ceremonial click
For each consequential step, state what the person is deciding. Is the reviewer confirming source accuracy, approving a client-facing communication, authorizing a record change, or accepting an exception? The workflow should stop when that decision is required. A reviewer who cannot see the relevant source or cannot reject the action is not exercising meaningful control. In a larger firm, different roles may approve different gates. In a small practice, one person may approve several. The key is that the authority is visible and the system behavior after rejection is known.
Boundary 5: design exception and stop behavior
A useful agent knows when not to continue
Agents will encounter missing information, ambiguous instructions, unavailable integrations, permission errors and conflicting sources. Define which conditions trigger retry, human escalation, safe fallback or shutdown. Do not let the system improvise around a missing authority simply because it can produce plausible text. For sensitive workflows, the exception policy should also cover data-boundary breaches and unexpected external connectivity. The firm's environment choice - cloud, local, segregated or selected air-gapped execution - should support the approved boundaries rather than contradict them.
Use the boundary card to scope architecture and development
Operational ROI: prevent rework before code and integrations multiply
A clear boundary card can reduce late-stage redesign because the team knows what it is actually building. It supports vendor evaluation, security review, testing, training and handover with the same vocabulary. Radsam's Agentic Automation Workflow Readiness and Framework Architecting work can use these boundaries to determine whether a workflow needs revision, a new architecture, development, or a lighter assistive design. The return is not a promise of perfect automation. It is a more disciplined path from business need to a bounded system that leadership can understand, test and change intentionally.
Author: Pouya Shafabakhsh Co-Founder, CAIO & Principal Forensic AI Auditor, Radsam Academy of AI Sovereign Governance. Independent AI governance and forensic-audit analysis. Educational information only; not legal advice.
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FAQ
What practical problem does AI agent boundaries solve for legal teams?
AI agent boundaries matters when it changes a legal, operational, or evidentiary decision. Start with the actual workflow and consequence. Then choose controls proportionate to the matter.
Which authorities should counsel verify before relying on AI agent boundaries?
Start with binding law, court rules, professional duties, and contracts relevant to AI agent boundaries. Separate guidance from mandatory authority. Recheck time-sensitive requirements before relying on them.
Which records best explain AI agent boundaries if later challenged?
Preserve the sources, material outputs, versions, reviewers, approvals, and exceptions tied to AI agent boundaries. Keep the record proportionate. It should explain consequential decisions without collecting everything.
Where should human review occur within AI agent boundaries workflows today?
Human review should occur before AI agent boundaries affects advice, evidence, filings, or external action. The reviewer needs authority to correct or stop the process. Record material overrides.
How should vendors support AI agent boundaries without expanding unnecessary risk?
Technology supporting AI agent boundaries should fit the defined workflow and data sensitivity. Review access, retention, security, exports, and vendor dependencies. Do not treat architecture as a legal guarantee.
When does independent review add value for AI agent boundaries decisions?
Independent review helps when AI agent boundaries raises unresolved technical or evidentiary questions. Define a narrow mandate first. Keep legal conclusions with counsel and technical findings within verified limits.
Which misconception most often distorts decisions about AI agent boundaries today?
Do not assume one tool, log, metric, or control settles AI agent boundaries. Context and corroborating evidence still matter. State technical findings proportionately and preserve important limitations.
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