AI Agent Development

AI Agent Development with Guardrails

We build AI agents that do useful, bounded work for Tucson organizations—with clear boundaries, human checkpoints, and defenses against common failure modes.

Goal and Boundary Definition

Every agent begins with a documented task, allowed inputs, expected outputs, and explicit non-goals. Tool and action allowlists, permissions, schemas, and approval gates are implemented as technical and operational controls, then tested for bypasses and failure modes. No control makes a model infallible, so actions outside the designed path are rejected, escalated, or placed into an exception workflow where the connected platform supports it.

Model, Tool, and Permission Selection

We choose models and tools based on your specific task, not on hype. We configure permissions at the least privilege level—the agent gets only the access it needs and nothing more. Data flow is mapped end-to-end, including retrieval sources, state management, and structured output schemas. We document every choice so your team understands what runs where.

Addressing Prompt Injection and Excessive Agency

Prompt injection and untrusted context are treated as design risks rather than problems that a single prompt can solve. External content is separated from trusted instructions where possible, tools use scoped permissions, and outputs are validated before downstream use. High-impact actions require human approval. Evaluation includes adversarial cases, unauthorized-action attempts, unreliable outputs, and ambiguous inputs, with residual risks documented.

Evaluation, Logging, and Deployment

Evaluation cases are defined before release and rerun as prompts, models, tools, and data sources change. Logs capture the inputs, outputs, tool requests, approvals, exceptions, and system events available within the selected architecture; they do not expose hidden model reasoning. Deployment is staged with rollback and recovery procedures. Documentation covers monitoring, ownership, updates, access review, and retirement without claiming error-free operation.

What the engagement can produce

Practical deliverables

Agent Boundary and Goal Specification

A written definition of the agent's task, allowed actions, and explicit non-goals.

Tool and Permission Configuration

A detailed list of tools, data sources, and permission levels, with least-privilege rationale.

Prompt-Injection Defense and Approval Flow

Documentation of how the agent handles untrusted input, plus the human approval gates for high-risk actions.

Evaluation Set and Deployment Log

A test suite of representative cases, plus a deployment checklist and logging schema for ongoing monitoring.

Need a bounded AI agent?

Let's define the task, the boundaries, and the human checkpoints that keep it safe.