Agentic AI systems.
Autonomy with accountability.

The demo succeeds because a human is watching, the inputs are clean and a failure is just a retry. Production is the opposite: inputs are messy, nobody is watching, and a wrong action writes to a system of record. The engineering that separates the two is not prompt craft—it is state management, tool design, permissioning, and knowing exactly where a human belongs in the loop.

Intellivetrix designs agentic systems around the blast radius of their actions. Read-only research agents get latitude. Agents that move money, change entitlements or contact customers get scoped credentials, deterministic pre-checks, approval gates and a complete audit trail. The substantive design work is deciding which is which, then building a runtime that enforces the distinction.

We treat evaluation as part of the system rather than a phase before launch. Agent trajectories are recorded, scored against expected outcomes and replayed whenever prompts, models or tools change—so you discover that a change broke a workflow in CI rather than in production.

Deterministic workflow steps wherever the process allows them; model reasoning only where it genuinely adds value. Most production agents need less autonomy than the prototype suggested.

Agents act with credentials scoped to the task, so a reasoning failure cannot escalate into an access-control failure.

Every step, tool call, input and decision is recorded, which is what turns an agentic process into one a risk owner can sign off.

Prompt, model and tool changes are validated against recorded trajectories before they reach users.

A system where a model plans and takes multiple steps, calling tools and enterprise APIs to complete a task, rather than returning a single response. The engineering challenge is not the reasoning but the surrounding runtime: state, permissions, validation, recovery and audit.

Primarily by constraining what it is able to do rather than by asking it not to. Agents receive credentials scoped to the task, actions pass deterministic policy checks before execution, and anything with a material blast radius sits behind an approval gate. Output filtering is a last line, not the main control.

Whenever the steps are known in advance. Model reasoning is valuable where the path genuinely varies with the input; everywhere else a deterministic workflow is cheaper, faster and easier to test. Most production systems we build are a mix, with autonomy confined to the parts that need it.

By recording full run trajectories and scoring them against expected outcomes rather than exact strings. Those recordings become a regression suite replayed whenever prompts, models or tools change, so behavioural drift is caught in CI.

The Model Context Protocol is an open standard for exposing tools and data to models through a consistent interface. It is useful when several agents or applications need the same enterprise integrations, because the tool is implemented once rather than per application. It is not a requirement for a single narrow use case.