Eager Intern Onboarding Model
Onboard AI agents with clear, simple, step-by-step instructions
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 97%
The Eager Intern Onboarding Model treats an AI agent like a motivated new colleague who works quickly but lacks context and judgment. The operator first identifies one bounded responsibility, then explains the desired outcome, sequence, constraints, and escalation conditions in ordinary language. Instructions should include details that matter operationally, such as keeping phone responses short, offering one appointment at a time, or asking what a prospect wants covered. Low-risk implementation details can remain flexible because the agent can often infer them. A realistic test interaction then exposes missing assumptions. Instead of searching for elaborate prompt syntax, the operator improves the briefing until the agent consistently understands and executes the assignment.
Origin
Flo Crivello presented the intern analogy while demonstrating how to configure a scheduling voice agent on Marketing Against the Grain.
Core principles
- 01Treat the agent as capable but inexperienced
- 02Define the exact outcome before writing instructions
- 03Explain the task in simple, sequential language
- 04Specify important conversational and operational constraints
- 05Let the agent infer low-risk details where appropriate
How to run it
- 1
Bound the assignment
Choose one discrete responsibility and define the result the agent must produce. Avoid assigning an entire function or job.
Pro tip Phrase the assignment as something you could reasonably delegate to a new intern.
Watch out A broad role such as “run demand generation” conceals too many decisions and dependencies.
- 2
Explain the workflow naturally
Describe what the agent should do in plain language and in the order it should do it. Include the information, tools, and actions needed to finish the task.
Pro tip Speak or write the briefing as if onboarding a real colleague.
Watch out Prompt formatting cannot compensate for an unclear understanding of the problem.
- 3
Add behavioral constraints
Specify the few rules that materially shape the experience, such as response length, sequencing, or escalation triggers.
Pro tip Use observable instructions such as “offer one availability at a time.”
Watch out Do not bury essential constraints beneath unnecessary detail.
- 4
Run a realistic test
Simulate a normal customer or employee interaction and inspect both the conversation and resulting system actions.
Pro tip Test the complete workflow, including calendar events, transfers, or follow-up messages.
Watch out A convincing conversation is insufficient if the downstream action fails.
- 5
Repair the briefing
Convert each observed failure into a clearer instruction, then retest. Continue until the agent performs the bounded assignment consistently.
Pro tip Change one instruction at a time so the effect is visible.
Watch out Do not respond to a narrow failure by adding a large amount of unrelated logic.
In the wild
The agent receives a prospect's information, checks a connected calendar, calls the prospect, offers one appointment at a time, asks what the prospect wants to discuss, creates the event, and reports the result. The prompt also requires extremely short utterances so the phone interaction does not become tedious.
→ A working appointment-booking workflow was assembled and demonstrated within minutes.
A restaurant agent is told to field incoming calls, answer questions about opening hours, and eventually connect to a reservation system. Testing reveals that it should use the current time to tell callers whether the restaurant is open now, producing a concrete instruction for the next iteration.
→ The test exposes a missing contextual instruction without requiring a redesign of the agent.
Common mistakes
Polishing an unclear prompt
Sophisticated wording does not help when the operator has not defined the actual problem, outcome, or constraints.
Assigning an entire job
An inexperienced agent cannot reliably own a broad function containing many implicit decisions and exceptions.
Skipping end-to-end testing
The agent may sound convincing while failing to create the event, update the system, or send the promised follow-up.
Is it for you?
Best for
It is best for teams configuring task-focused agents without specialist prompt-engineering expertise.
Not ideal for
It is not ideal for poorly understood jobs whose goals, boundaries, or success criteria remain ambiguous.
From the transcript
“I tell people to think of these agents as like an intern.”
“You are onboarding a very eager intern, not a ton of experience, not the smartest guy ever, but very eager.”
“speak naturally, like I just did with my disgusting franchise and and it just works.”
From the episode
Everything You Need To Know About AI Voice Agents in 2025