Security Team

Engineering team at Bothive. Building the future of AI agent orchestration.

The Alignment Problem

In Enterprise, "creative" AI is a liability. You don't want your support bot creatively inventing a new refund policy.

The Sandwich Approach

We wrap the LLM in two deterministic layers.

Pre-Flight Check: Sanitizes inputs (PII redaction).
The LLM: Generates the response.
Post-Flight Check: Validates the output against business rules.

typescript
if (response.contains("refund") && !user.isEligibleForRefund) {
   return "I cannot process a refund, but I can offer credit.";
}

We use a rule engine (JsonLogic) to define these constraints. The Agent is free to be creative within the box, but it can never step outside the box.

How to apply this inside Bothive

The practical move is to turn the idea into an agent contract: what the agent can see, what it can do, where it should ask for approval, and how the team will inspect the result. A good Bothive workflow is not just a prompt. It has memory, tools, channels, traces, and a clear boundary between autonomous work and human judgment.

Define the boundary

For security work, decide which decisions the agent can make alone and which actions need a teammate in the loop.

Attach real context

Connect docs, customer data, repositories, tickets, calendars, or APIs so the agent works from grounded information.

Ship through a channel

Expose the agent through web chat, API, Slack, WhatsApp, schedules, or internal workflows depending on where the work starts.

Watch the run

Use traces, tool-call history, usage, and failure logs to improve the agent after it meets real users.

Build

Turn the idea into a readable agent contract, workflow, or builder graph.

Deploy

Run it through Bothive channels, schedules, integrations, and API calls.

Observe

Use traces, usage, memory, and tool logs to improve the system over time.

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Solving the Alignment Problem in Enterprise