Most B2B teams deploy an AI agent, connect it to their knowledge base, and assume the communication layer will sort itself out. It won't. Without someone deliberately shaping how your agent speaks, it defaults to patterns baked into the underlying model: verbose when brevity would do, neutral when warmth is called for, and slow to hand off when the customer is already frustrated.
Conversation design is the discipline of defining how your AI agent communicates: its tone, response structure, handoff logic, and escalation behavior. Without a designated owner for these decisions, the agent makes them itself, usually in ways that erode customer trust even when the answers are technically correct. The fix is to write down how you want the agent to sound, design the handoff as carefully as you design the resolution, and treat communication quality as a measurable output, not a default setting.
What conversation design actually means
The term sounds like something a UX team handles once and files away. It isn't. Conversation design is an ongoing operational function that covers five distinct areas:
Tone and personality. How formal or casual does the agent sound? Does that shift based on the situation, a billing dispute versus a product question? The agent should have a consistent voice but a flexible register.
Response structure. Does the agent match the depth of its answer to what the customer actually asked? A one-line question rarely deserves five paragraphs. An agent that over-explains trains customers to skim, and skimming means missed resolutions.
Handoff logic. When does the agent escalate, how does it communicate that transition, and what context carries over to the human rep? A poor handoff means the rep inherits a frustrated customer who has to repeat themselves.
Interaction flow. How does a conversation move from question to answer to resolution or escalation? Flow problems show up as circular conversations where the customer keeps rephrasing the same thing because the agent isn't actually resolving it.
Response quality. Does the answer feel clear, helpful, and on-brand even when it's technically correct? An accurate answer delivered awkwardly still erodes trust.
Intercom published results from an A/B test of their Fin AI agent's opening message: one warm and conversational, one a flat default. The warmer version lifted CSAT measurably, before the agent had answered a single substantive question. A single sentence changed at the conversation entry point produced a measurable shift in customer satisfaction. That is what conversation design is working with.
Where to start
Write down how the conversation should feel
Before tuning individual responses, define the voice. One paragraph is enough. You don't need a brand guidelines document. You need a reference point your team can come back to when making decisions. "Clear and direct, never condescending, warmer when the customer signals stress" is a workable starting point. "Professional and helpful" is not, because it describes every agent ever shipped.
Different conversation types may need different registers while keeping the same voice. A customer locked out of their account needs speed and directness. Someone evaluating a new feature might want more context. The voice stays consistent; the register adapts to what the moment calls for.
If you already think carefully about how your LinkedIn presence communicates your team's expertise, apply the same thinking here. The operators we audit who build strong inbound pipelines through content have usually already wrestled with the question of what they sound like. That same judgment transfers directly to agent design. For more on how communication choices affect how buyers perceive you, see our piece on how to build inbound pipeline through LinkedIn engagement.
Design the handoff as carefully as you design the resolution
The transition from agent to human rep is the highest-friction moment in an AI-assisted support flow. Most teams design the resolution path carefully and then treat the handoff as a fallback they'd prefer not to trigger. That's backwards.
The rep should receive: the full conversation history, the context behind the issue, what the agent already tried, and why the escalation happened. The customer should not have to repeat a single word.
The language the agent uses at the handoff point also matters more than most teams expect. "Let me connect you with a teammate who can help with this" is a different customer experience than a silent transfer or a generic "transferring now." The former keeps the customer oriented. The latter signals that the system has given up.
Build a failsafe too. If the agent can't resolve the conversation cleanly, what is the fallback path that still gives the customer a smooth experience? A customer who reaches a dead end inside the agent and gets no graceful exit becomes a customer who doesn't trust the agent the next time.
Assign an owner
The biggest structural gap we see in teams that have deployed AI agents is that nobody owns conversation design. The product team owns the agent configuration. The support team owns escalations. Nobody owns the communication layer that connects them.
This doesn't require a dedicated headcount in a new role. It requires that someone, specifically, is responsible for:
- Maintaining the voice reference document
- Reviewing a sample of agent conversations weekly
- Making calls about tone, structure, and handoff language when edge cases emerge
- Treating CSAT on agent-handled conversations as their number to improve
Without that owner, the agent drifts. Model updates change behavior. New content added to the knowledge base shifts the response style. What felt consistent at launch starts to fragment.
How to measure whether it's working
Conversation design isn't subjective once you decide what to measure. The metrics that matter:
CSAT on agent-handled conversations. The baseline before any design work is your benchmark. Track it after each meaningful change.
Escalation rate. A high escalation rate can mean the agent is resolving too little, but it can also mean the handoff logic is too eager. Separate the two by looking at escalations where the rep resolved the issue without adding new information. Those are design failures, not knowledge gaps.
Re-open rate. If customers come back to the same issue shortly after the agent marked it resolved, the resolution didn't land. Often this is a communication problem, not an accuracy problem.
Conversation length before resolution. Longer isn't better. If the agent is taking twelve messages to resolve what should take three, the interaction flow has a problem.
Skip rate. If your product gives customers the option to bypass the agent and go straight to a human, track who takes it and when. High skip rates in specific conversation categories tell you where the agent's communication isn't trusted.
What gets easier once you do this
Once conversation design has an owner and a voice reference exists, a lot of decisions that currently require a meeting stop requiring one. When the model behaves unexpectedly after an update, the owner can evaluate it against a written standard. When a customer complains about the agent's tone, there's a benchmark to audit against. When you add a new product area to the knowledge base, you have guidance on how responses in that category should be structured.
Customers who get consistently clear, well-structured responses from an agent start to trust the agent. They escalate less. They re-open fewer tickets. They don't skip the agent to reach a human who will tell them the same thing. Every one of those outcomes shows up in your support metrics once the changes are live.
The same discipline applies when you think about how your team communicates expertise externally. Deliberate choices about register, structure, and how to handle a conversation that needs to change direction are the same whether the channel is a support chat or a LinkedIn comment. See how we think about the distinction between engaging and posting on LinkedIn for a related look at intentional communication choices.
The agent you have right now is making communication decisions on your behalf. The only question is whether those decisions are being made to a standard you set, or to defaults you inherited.
</p> <h2> <a name="faq" href="#faq" class="anchor"> </a> FAQ </h2> <p><strong>What is conversation design for AI agents?</strong></p> <p>Conversation design is the practice of defining how an AI agent communicates with customers: its tone, how it structures responses, when it escalates to a human, how it handles the handoff, and how it adapts its register to different types of conversations. Without deliberate conversation design, the agent defaults to patterns set by the underlying model, which often don't match how your team actually communicates.</p> <p><strong>How do I make my AI agent sound more like my brand?</strong></p> <p>Start by writing a short voice reference document, one paragraph describing how you want the agent to sound across different situations. Cover tone, level of formality, and how the register should shift when a customer is frustrated versus exploring. Review a sample of real agent conversations weekly against that reference and adjust the guidance when you see drift. The goal is a written standard your team can evaluate against, not just intuitions held in someone's head.</p> <p><strong>What metrics should I track to measure AI agent communication quality?</strong></p> <p>The most useful metrics are: CSAT on agent-handled conversations (your baseline before any design work), escalation rate broken down by whether the human rep added new information, re-open rate on agent-resolved tickets, average conversation length before resolution, and skip rate if your product lets customers bypass the agent. Together these separate communication problems from knowledge gaps.</p> <p><strong>When should an AI agent hand off to a human support rep?</strong></p> <p>Handoff logic should be defined before you deploy, not discovered in production. Trigger escalation when the agent has attempted resolution without success after a defined number of exchanges, when the customer signals frustration explicitly, or when the issue category falls outside the agent's reliable scope. The handoff itself should always carry full conversation context to the rep so the customer does not have to repeat themselves, and the agent's language at the transition point should keep the customer oriented rather than just dropping them into a queue.</p> <hr> <p><em>This article was first published on the <a href="https://www.getchime.co/blog/conversation-design-ai-agent?utm_source=devto&utm_medium=republish">Chime blog</a>. For more on LinkedIn pipeline tactics, <a href="https://www.getchime.co/blog?utm_source=devto&utm_medium=republish">visit the Chime blog</a>.</em></p>
