Parallax/Services/Agents & automation
AI agents and automation, with a human on the loop
Parallax builds AI agents that use your tools, MCP servers that connect models to your systems, and multi-step automations that take work off people’s plates — with approvals, audit logs and least-privilege access designed in. When a manual process, spreadsheet or SaaS seat is costing more than it should, we replace it with focused internal software.
Service
What can AI agents automate?
The best candidates are workflows that mix clear rules with judgment calls a person makes dozens of times a day: triaging requests, researching and summarizing, reconciling records across systems, drafting responses, classifying and routing content, or preparing reports. Models now handle the interpretation, classification and writing steps that used to block automation.
- Tool-using agentsAgents that read, look up, draft and act through your APIs — within limits you set.
- MCP serversSecure Model Context Protocol servers that expose your CRM, ticketing, data or internal APIs to AI assistants and agents.
- Multi-step pipelinesDeterministic workflows with AI steps where they add value, and plain code everywhere else.
- SaaS & spreadsheet replacementFocused internal tools that do one job better, cheaper and closer to your data.
What is an MCP server, and do we need one?
The Model Context Protocol is an open standard for connecting AI applications to tools and data. An MCP server wraps a system — your CRM, a database, an internal API — so any compatible assistant or agent can use it with consistent authentication and permissions. If your teams use AI assistants daily, a few well-designed MCP servers are often the highest-leverage automation you can build.
How do we keep agents safe?
- Least privilege: each tool gets only the scopes it needs; read and write are separate.
- Approvals where it matters: agents propose, people approve anything irreversible or customer-facing.
- Explicit tools, visible reasoning: we favor observable systems over opaque autonomy, so every action is explainable.
- Audit logs and dry runs for every action, plus evaluation suites that catch regressions before release.
Can AI replace our SaaS tool or manual process?
Sometimes. We map how the work is done today, where the source of truth lives and what the current tool really costs, then compare that with a focused internal application — including maintenance. When the numbers favor building, we build; when they don’t, we say so.
Questions
What is the difference between an AI agent and an automation?
An automation follows fixed steps. An AI agent decides which steps and tools to use to reach a goal. We usually combine them: deterministic pipelines for predictable work, and agents only where flexibility is worth the extra oversight.
Which frameworks do you use for agents?
We choose per project and often use provider SDKs directly, because thin, explicit code is easier to test and debug than heavy frameworks. We are comfortable with the major agent frameworks when they fit.
How do you measure whether automation worked?
We baseline the workflow before we build — hours spent, turnaround time, error rate, cost — and measure the same numbers after release, so the result is a number, not an impression.
Updated 2026-10-23