1. Data and identity controls
The agent should know who the user is, what that user is allowed to access, and which records are outside the current task.
- Authenticate every user and service call; do not rely on a hidden URL.
- Enforce authorization in the database or API, not only in the prompt.
- Use separate tenant boundaries for separate customers.
- Remove secrets, unnecessary personal data, and irrelevant records from model context.
- Define retention and deletion rules for prompts, files, outputs, and logs.
- Document which model providers can receive which categories of data.
2. Prompt-injection and content controls
Treat all retrieved content as untrusted data, even when it comes from a company document or familiar website.
- Keep system instructions separate from retrieved text and user content.
- Tell the agent that content cannot grant permissions or redefine its role.
- Allowlist tools, domains, file types, and data sources.
- Scan uploaded and retrieved content before it enters a privileged workflow.
- Require source references for factual answers when the workflow depends on retrieved knowledge.
- Test direct, indirect, encoded, multilingual, and multi-step injection attempts.
3. Tool and action safety
The model may propose an action, but deterministic software should decide whether that action is valid and authorized.
- Use narrow tools with typed inputs instead of broad shell, database, or browser access.
- Validate amounts, recipients, identifiers, dates, and state transitions outside the model.
- Make high-impact tools read-only by default and require explicit approval for writes.
- Use idempotency keys so retries do not duplicate emails, payments, records, or orders.
- Set rate, spend, time, and recursion limits for each run.
- Record the user, model version, prompt version, tool call, approval, and final outcome.
4. Evaluation before release
A demo is not an evaluation. Use a versioned test set built from normal cases, edge cases, known failures, and adversarial inputs.
- Define a pass/fail rubric for the complete business task.
- Score factual support, extraction accuracy, format validity, and correct tool choice.
- Measure unsafe action attempts, unnecessary refusals, and escalation quality.
- Run the same test set when the model, prompt, retrieval system, tool, or data changes.
- Include human review for subjective quality and customer-facing tone.
- Set release thresholds before looking at the results.
5. Monitoring and incident response
Production monitoring should reveal quality drift and operational risk, not only whether the server is running.
- Track success rate, fallback rate, approval rejection, unsupported claims, latency, and cost per completed task.
- Alert on unusual tool use, permission failures, repeated retries, volume spikes, and sensitive-data patterns.
- Keep a one-click way to disable actions or switch the workflow to manual review.
- Version prompts, tools, models, policies, and knowledge sources so an incident can be reproduced.
- Define who reviews failures, who can pause the system, and how affected records are corrected.
- Review samples of successful runs; silent quality loss often appears before outright errors.
Common questions
Frequently asked questions
Should an AI agent have direct database access?
Usually no. Give it narrow, authenticated API tools that enforce authorization and validate inputs. Direct broad database access increases the blast radius of a bad decision or injection attack.
How do you prevent an AI agent from taking the wrong action?
Constrain its available tools, validate proposed actions in code, require approval for consequential steps, set hard limits, and test the complete workflow against normal and adversarial cases.
How often should an AI agent be evaluated?
Run the fixed evaluation set before every meaningful change to the model, prompt, retrieval system, tools, or data. Monitor real production samples continuously for drift and new failure modes.
What belongs in an AI agent audit log?
Record the requesting user, relevant inputs, model and prompt versions, retrieved sources, proposed and executed tool calls, approvals, errors, timing, cost, and final business outcome.
Put this into practice
Start with one useful, controlled workflow.
Studio X scopes the smallest production-ready system that can create a measurable result, with owner approval and a practical fallback built in.