Best Practices for Instructional Designers

Instructional designers working in Outdoo are not building content to be consumed — they are building scenarios to be practiced. The design challenge is different. This guide covers what makes a roleplay agent behave realistically, how to write scorecards that produce actionable data, and how to structure simulations that transfer to live performance.

Designing roleplay agents

Realism is the only thing that matters

A rep who passes a practice session against a predictable, easy-to-handle AI agent has not developed a skill. They have gotten comfortable with a simulation that does not resemble their actual job. Every design decision in a roleplay agent should serve one question: does this feel like a real buyer?

Start from a real call, not a persona document

The best agents come from actual customer calls — specifically the hard ones. Upload a call where the rep lost momentum, where a new objection surfaced, where the buyer was unusually skeptical. Outdoo generates an agent from that interaction. Reps practicing against it are rehearsing for the actual situation, not a textbook version of it.

Write persona descriptions that constrain the AI specifically

Vague personas produce inconsistent agents. "Skeptical buyer" is not a persona — it is an adjective. A useful persona specifies:

• Role, seniority, and company context (VP of Sales, 200-person SaaS, currently using a competitor)
• Emotional starting state (frustrated from a bad prior vendor experience, or cautiously interested)
• What they know and what they do not (aware of the problem, not aware your product exists)
• The specific objections they will raise and in what order (leads with budget, then circles back to integration concerns)
• What would make them more open (a specific ROI framing, a specific proof point they find credible)

Use scenarios to set the buyer's starting state, not just their role

A VP of Sales who is actively evaluating solutions behaves completely differently from a VP of Sales who does not know they have a problem. The persona describes who they are. The scenario describes where they are in their journey. Both are required for a realistic simulation.

Configure behavior constraints, not just personality

The behavior settings let you control how the agent responds to different rep actions: how resistant they are to pivots, whether they ask follow-up questions or just answer, how quickly they reveal objections, and what triggers their interest. These settings make the difference between an agent that feels like a real conversation and one that feels like a quiz.

Add resources to the agent to ground it in your actual content

If you have battlecards, product briefs, or objection-handling guides, attach them as resources. The agent uses these to ensure its responses and objections align with what your reps have been trained on — not generic sales objections from its training data.

Always preview before publishing

Run the agent yourself before assigning it to reps. Specifically test: does the AI stay in persona under pressure? Does it raise the objections you configured? Does it respond appropriately when the rep gives a good answer? An agent that behaves inconsistently in preview will behave inconsistently in practice.

Designing scorecards

Score the behavior, not the outcome

The most common instructional design mistake in Outdoo scorecards is measuring what happened rather than how it happened. "Did the rep set a next step?" is an outcome. "Did the rep confirm the next step using the buyer's own calendar availability?" is a behavior. Scoring behaviors gives you a cause. Scoring outcomes gives you a result you already knew from the CRM.

Every criterion maps to a specific playbook behavior

Before writing a scorecard criterion, find the corresponding line in your sales playbook or call guide. If you cannot point to a specific, documented behavior the rep is expected to demonstrate, the criterion does not belong on the scorecard. Scorecards should be a machine-readable version of your playbook, not a list of general sales skills.

Use the distribution settings to weight what matters most

Not all criteria carry equal weight. Discovery question quality may matter more than talking speed for your team. Configure the point distribution to reflect actual priorities — a criterion worth 5% of the score will not change coaching behavior, even if it appears on the card.

Use the same scorecard for roleplay and live calls

Outdoo applies your scorecard to both practice and real conversations. This consistency is the feature that makes training data meaningful — you can compare a rep's discovery score in roleplay to their discovery score on real calls. Different scorecards for practice and live breaks this comparison and removes the signal you need to measure transfer.

Attach resources to the scorecard for in-context coaching

You can attach documents directly to a scorecard — objection-handling guides, talk tracks, example calls. When a rep reviews their score and sees they dropped points on a specific criterion, the relevant resource is right there. This removes the gap between "I got a low score" and "I know what to do differently."

Designing micro-learning agents

Use micro-learning for targeted reinforcement, not initial learning

Micro-learning agents drop reps into a single moment — the pricing objection, the next-steps ask, the competitive differentiation question — and score them on just that moment. They are not replacements for full-call roleplay. They are reinforcement tools: use them after a full training program to maintain and sharpen specific skills, or during a call blitz to drill one skill fast.

Keep micro-learning agents under 3 minutes

A micro-learning agent that runs longer than 3 minutes is a full roleplay with a different name. The value of micro-learning is that it fits into a gap in the day. Design them to start at the relevant moment, handle 2–3 exchanges, and end. If the scenario needs more than that to make sense, it belongs in a standard agent.

Designing workflow simulations

Document the correct path before building anything

The simulation builder requires you to define the correct sequence of actions — every field, every click, every screen transition — before you can build the scoring. Walk through the real workflow yourself and write it down step by step before opening the builder. The most common cause of simulation data quality issues is an ambiguous or incomplete correct path definition.

Score mandatory fields at higher weight than optional ones

In simulation scoring, a missed required field that causes a compliance issue or a broken downstream process deserves more weight than an optional field left blank. Configure scoring weights to reflect business risk, not uniformly equal weighting across all steps. A simulation where every step is equal teaches reps that every step matters equally — which is not true and is not what you want them to internalize.

Keep each simulation to a single workflow

One simulation per workflow — not one simulation covering all post-call tasks. A 20-step simulation covering the entire CRM update process gives you aggregate accuracy. A 6-step simulation covering just the opportunity stage update gives you specific, actionable data on exactly where errors happen. Build small and specific. You can always add more simulations.

Test for errors of omission, not just errors of commission

The most common mistakes in post-call workflows are not doing the wrong thing — they are not doing a required thing at all. When configuring simulation scoring, explicitly flag steps where skipping is the likely failure mode (required fields, mandatory disposition selection). Do not assume the scoring will catch omissions automatically — configure it to catch them.

Testing and iterating your designs

Run agents and simulations yourself before any rep does

Walk through every piece of content you build as if you were the learner. For agents: does it feel like the right buyer? Does it raise the objections you intended? For simulations: does the interface match the real system closely enough that practice transfers? You will catch design problems in five minutes that would take weeks to surface in rep feedback.

Use early cohort data to improve design, not just measure performance

The first cohort through any new program reveals instructional design gaps. A low score on a specific scorecard criterion might mean reps lack the skill — or it might mean the agent is not testing for that skill effectively, or the criterion is ambiguously written. Investigate before drawing conclusions from the data.

If you need help, contact us at support@outdoo.ai.