Your AI agents deserve a probation period

1 July

Here’s a hard truth: most businesses trust AI agents too soon.

In the rush to scale agentic AI, many teams treat new agents like plug-and-play software. They wire up a Copilot, throw it live, and assume it’ll just ‘learn’. But here’s the kicker, agents don’t improve themselves in production. They repeat mistakes. They drift. They hallucinate. And the more you scale, the bigger the mess.

In Agentforce 3, you’re not dealing with one Copilot, you’re orchestrating an entire team of specialised AI agents, each handling different parts of your sales or service flow. Think of them as digital colleagues: a triage agent here, a case summariser there, a product recommender somewhere else. Each one does real work that touches real customers.

Would you let a new hire loose on your most sensitive workflows without onboarding or feedback? Of course not. So why do it with your AI?

Introducing the Continuous Agent Testing Centre

At We Lead Out, we’ve seen this firsthand. Multi-agent orchestration is where Salesforce AI is heading, but trust comes from rigour, not blind faith. So, we treat AI agents like people. Every new agent goes through a probation period before it goes fully live.

The method is simple but powerful:

1. Shadow mode by default

Before any agent starts interacting with customers solo, it runs in shadow mode alongside your human team. It generates outputs, but a human checks them before anything is sent. Think of it like an apprentice watching and drafting work, but not pressing ‘send’ on their own.

This does two things. One, you protect your customer experience. Two, you generate real data on how well the agent performs under true production conditions.

2. Benchmark harnesses and regression checks

A fancy term for test packs. Just like you’d run test cases for new Salesforce functionality, you should design prompt harnesses that simulate real scenarios, the tricky edge cases, the mundane repeats, the high-volume flows. If an agent starts drifting, you’ll spot it fast.

Too often, teams rely on anecdotal reports: “It seemed off last week.” That’s not good enough. Automated benchmarks catch drift and bias before they show up in your KPIs.

3. Feedback loops from real users

Every correction, edit, or override a human makes while the agent is in shadow mode should be logged and analysed. This data is gold. It tells you how to refine the agent’s prompt design, context windows, or retrieval chain.

It also surfaces design flaws in your Salesforce setup, messy data, vague fields, or patchy knowledge bases can tank an agent’s accuracy. The testing centre closes that loop.

4. Tie every output to a Salesforce record

When something goes wrong, and it will, you want an audit trail. One of our non-negotiables is wiring agent outputs to standard or custom Salesforce objects. That means you can always see what the agent said, what data it used, who approved it, and what the final outcome was.

It’s not about catching your AI out. It’s about accountability, the same accountability you’d expect from a team member.

5. Continuous reporting on ‘agent health’

Once an agent passes probation and goes fully live, the work isn’t done. We set up dashboards that track agent performance over time. Is accuracy holding up? Are we seeing more overrides than last month? Are new edge cases popping up?

If an agent’s performance dips, you know exactly when to pull it back into shadow mode, update prompts, or retrain your model. This is the heartbeat of trust, measurable, visible, and continuous.

Why this matters more than ever

Agentforce 3 isn’t theoretical. We’re already seeing Salesforce customers launch multiple domain-specific agents: a deal triage Copilot, a knowledge lookup assistant, a claims estimator. Each agent plugs into core Salesforce data, acts on behalf of your brand, and impacts conversion, CSAT, and compliance.

One hallucinated claim estimate can erode trust fast. One biased lead score can skew your pipeline for months.

This is why the Continuous Agent Testing Centre isn’t a nice-to-have. It’s your guardrail against the risks that come with scale.

And when it’s working well, it’s a flywheel for improvement: you build better prompts, cleaner data, and smarter flows, so your next agent deploys faster, cheaper, and safer.

Three things you can do this quarter

Want to put this into action? Here’s what I’d do first as a Solution Architect:

1. Pick your first agent and design a shadow mode plan

Take your highest-volume, lowest-risk use case, like an FAQ or internal knowledge assistant. Run it in shadow mode for four weeks. Log every correction. What patterns emerge?

2. Build your benchmark harness

Write test prompts that mirror real user questions, weird phrasing, or edge scenarios. Automate them. Run them on a schedule. Treat your agents like software, not just smart templates.

3. Wire up audit trails

If you haven’t yet, make sure every agent interaction logs to a Salesforce object, even if it’s just a simple custom record. You’ll thank yourself later when you need to prove what the agent said (or didn’t say).

Final thought: trust isn’t free

It’s earned, and it’s fragile. If you want to orchestrate an army of AI agents that act like trusted teammates, you need to give them the same attention you’d give your people.

Shadow mode. Benchmarks. Feedback loops. Clear governance.

That’s how you get Agentforce 3 right the first time. And it’s how you keep your AI trustworthy — no matter how fast you scale.