Your AI Agent's Long Responses Are a Bug, Not a Feature

Source: Dev.to

The Real Problem with Verbose Agents

When an agent generates long, explainer‑style output for every task, it means one of three things:

• The task definition is vague. The agent doesn’t know what “done” looks like, so it covers all possible interpretations.
• The prompt rewards explanation over execution. You told it to “think carefully” and “explain your reasoning,” and now it can’t stop.
• Nobody defined the output format. The agent is improvising length and structure on every run.

All three are configuration problems, not model problems.

Brevity Is a Config Skill

The agents we run at Ask Patrick have one thing in common: they know exactly what format their output should take before they start.

• Our growth agent doesn’t write multi‑paragraph explanations of why it’s drafting a tweet. It drafts the tweet.
• Our ops agent doesn’t narrate its health‑check logic. It returns a one‑line status.

Example comparison

Verbose agent (bad config):

“I’ve reviewed the task requirements and after careful consideration of the available options, I believe the most appropriate course of action would be to draft a tweet that speaks to the target audience’s pain points while also highlighting the unique value proposition of Ask Patrick. Here is a potential draft…”

Crisp agent (good config):

“DRAFT: [tweet text here]”

Same model. Completely different configuration.

The Fix

Add one line to your agent’s system prompt:

Be concise. Return output in the exact format specified. No preamble. No summary. No explanation unless explicitly asked.

Then define the output format explicitly for each task type. Don’t use “respond naturally” — specify the schema.

For tweets:   Format: One tweet under 280 chars. No commentary.
For reports: Format: Status (OK/WARN/FAIL), one-line summary, optional details.
For analysis: Format: 3 bullets max. Each bullet = one actionable insight.

The agent stops padding when it knows precisely what it’s supposed to produce.

Why This Matters at Scale

We run five agents on a Mac Mini. Every verbose output is:

• More tokens → higher API costs
• More text to parse → slower downstream agents
• More surface area for hallucination

When we tightened our output specs across the whole team, token usage dropped ≈ 30 % with zero loss in output quality. The agents became faster, cheaper, and easier to debug.

The full output‑format patterns we use are in the Library at askpatrick.co/library.

If your agent explains everything it’s about to do before it does it, that’s a config problem. The fix takes five minutes.