What Is This Feature?

An "agent" in this platform is more than just the AI model — it's a configured personality, a set of rules, a defined scope of knowledge, and a collection of policies about how it should behave. Should it always cite sources? Should it refuse to answer questions outside a specific topic area? Should it respond in a certain tone? All of this is captured in the agent configuration. This deep dive explains how agents are defined, how changes are made safely, and how you can test improvements before rolling them out to all users.

Why It Matters to Your Business

Your AI agent is a customer-facing product. The way it behaves — what it says, how it says it, what it refuses to do — directly shapes your customers' experience and your brand reputation. Changing that behavior carelessly can introduce regressions: an improvement for one use case might break another. This feature provides the guardrails that make confident, safe iteration possible.

Customization for every use case. A support agent for a software product behaves differently than a sales agent for a financial services firm. The configuration system lets you define exactly how each agent should behave for its specific context.
Safe updates. Before a change to agent behavior reaches all users, it can be tested on a small percentage of traffic. If metrics look good, roll it out further. If something looks wrong, roll back instantly — no code deployment required.
Accountability. Every change to an agent is versioned and audited: who changed it, what changed, when. This is important for compliance and for understanding the history of your product's behavior.
Quality validation. Changes to agent instructions go through automated checks before they can be saved, catching common mistakes before they reach users.

How It Works (No Technical Jargon)

An agent configuration includes:
- A name and description — what this agent is for
- Behavioral policies — rules like "always include sources", "don't answer questions outside these topics", "escalate to a human agent if the user expresses frustration"
- Knowledge connections — which knowledge bases this agent can draw from
- Tone and format preferences — how responses should be structured

When you update an agent's configuration:

Automated checks run first. The system validates that the changes are well-formed and don't contain anything that could cause problems.
A new version is created. The previous configuration is preserved — you can always see the history and roll back to any prior version.
An audit entry is created. The system records who made the change, what changed, and when.
The new version can be tested on a slice of traffic. For example, you might route 10% of conversations to the new version while the other 90% continue on the current version. You watch the metrics — citation accuracy, user satisfaction, response quality — and decide whether to proceed.

A canary rollout is a way of testing a change in production with real users before committing to it fully. Think of it as a controlled experiment: the new agent behavior runs alongside the old one, and you compare results. If the new version performs better (or at least no worse), you gradually increase its share of traffic. If something looks off, you revert with a single click.

Version History and Rollback

Every version of every agent is preserved. From the admin dashboard, you can:
- See a timeline of all changes made to an agent
- Compare any two versions side by side
- Restore any previous version with a single action
- Understand the impact of past changes by correlating version changes with quality metrics

What to Expect on the Roadmap

The team is building:

Full version history and audit trail for all agent changes (estimated 2 weeks)
A canary rollout controller with a percentage slider, live metrics, and one-click rollback (estimated 4 weeks)

Once live, you'll have a safe, structured way to continuously improve your agent's behavior — without the risk of breaking things for all users at once.