Agente IA enterra a resposta (respostas longas demais)

Agente IA enterra a resposta (respostas longas demais)

Você é founder/CEO de SaaS.

Seu SaaS: agente IA em produção (WhatsApp, email, chat).

Seu cenário (muito real):

• Customer: "Como eu reseto a senha?"
• Agente response: [walls of text]
  • Parágrafo 1: Explicação geral sobre senhas
  • Parágrafo 2: Contexto histórico (por que senhas existem)
  • Parágrafo 3: Segurança (dicas de senha forte)
  • Parágrafo 4: O que evitar (não compartilhe a senha)
  • Parágrafo 5: Troubleshooting (se não funcionar)
  • Parágrafo 6: Links relacionados (outros recursos)
  • Parágrafo 7: Oferecimento de suporte adicional
• Customer reality: Leu parágrafo 1, desistiu (muito texto, não lê tudo)
• Customer action: Vai pra concorrente (que responde em 1 frase)
• Your loss: Churn (por culpa do agente ser verboso)

Your nightmare:

• "Meu agente responde tudo... mas responde MUITO."
• "Respostas longas, muita informação, customer não lê."
• "Resultado: Customer abandona conversa, churn aumenta."
• "Problema: Agente é inteligente mas comunica mal."

Breaking insight (developer community, 2026):

• Problem: LLM agents are verbose by default (trained on verbose data)
• Symptom: "I ask question, agent buries answer in 5 paragraphs"
• Pattern recognition: Like someone with ADHD reading a wall of text (can't find main point)
• Solution: Prompt skill "I-have-ADHD" (force concise, direct answers)
• Result: Answer first, context second (if asked) = customer happy

Why this matters (your business):

Verbose response (current): ├─ Customer reads: Parágrafo 1 only ├─ Customer understands: Maybe 20% (still confused) ├─ Customer action: Gives up (frustration ↑) ├─ Customer result: Churns or goes to competitor └─ Your loss: Revenue gone

Concise response (optimized): ├─ Customer reads: All of it (short enough) ├─ Customer understands: 100% (clear + direct) ├─ Customer action: Problem solved (satisfaction ↑) ├─ Customer result: Stays (retention ↑) └─ Your win: Revenue protected

---

Why agentes become verbose (root cause)

Problem 1: LLMs trained on verbose data

How LLMs learn:

Training data: Mostly human-written text (blogs, docs, articles) ├─ Style: Formal, thorough, over-explained ├─ Example: "To reset your password, first you need to understand what a password is..." ├─ Result: LLM learns to write like that (verbose) └─ Outcome: When you ask LLM to answer, it mimics training data (verbose)

Why human writers are verbose: ├─ SEO (longer content = more keywords = better ranking) ├─ Trust (more words = more authoritative) ├─ Completeness (cover all edge cases) ├─ Liability (over-explain to avoid misunderstanding) └─ Result: Internet is full of verbose content

Side effect: ├─ LLM trained on verbose content ├─ LLM naturally verbose (mimics training data) ├─ You: "Answer this question" ├─ LLM: "Here's 5 paragraphs (even though answer is 1 sentence)" └─ Customer: Frustrated (too much text)

Problem 2: Agente tries to be "helpful" (over-help)

Why agentes over-explain:

Agente logic: ├─ "Customer asked 1 question" ├─ "I should provide context (be helpful)" ├─ "I should add related info (anticipate follow-ups)" ├─ "I should explain WHY (not just WHAT)" ├─ "I should cover edge cases (what if customer...)" ├─ "I should provide next steps (what to do after)" ├─ Result: Response = 5 paragraphs (trying to be thorough)

Why this is bad: ├─ Customer didn't ask for all that ├─ Customer wanted: Answer (direct, fast) ├─ Customer got: Answer + context + explanation + edge cases + next steps ├─ Customer feeling: Overwhelmed (too much info) ├─ Customer action: Ignore most of it (reads only first few lines) └─ Result: Goal backfired (trying to help, customer frustrated)

Problem 3: No constraint on response length

How prompts usually work:

Standard prompt: ├─ "You are a customer support agent." ├─ "Answer customer questions helpfully." ├─ "Be thorough and complete." └─ "No constraint on length"

Result: ├─ Agente interprets "be thorough" = write everything ├─ Agente writes: 1000+ words (covering all possibilities) ├─ Customer reads: 50 words (gave up halfway) └─ Goal: Fail (customer didn't get what needed)

Better prompt: ├─ "You are a customer support agent." ├─ "Answer customer questions directly." ├─ "Answer first (1-2 sentences)." ├─ "Then context (if customer asks for more)." ├─ "Keep responses under 2 paragraphs." └─ Result: Agente is concise (customer reads all)

---

The "I-have-ADHD" pattern (how to fix)

What is "I-have-ADHD" skill?

The pattern (GitHub insight):

Pattern name: "I-have-ADHD"

Goal: Force agent to answer like someone with ADHD reading ├─ No patience for long intros ├─ No tolerance for unnecessary context ├─ Need answer FIRST (immediately) ├─ Accept context ONLY if requested └─ Prefer lists over paragraphs (scannable)

How it works: ├─ Step 1: Customer asks question ├─ Step 2: Agente finds answer (identify core insight) ├─ Step 3: Agente states answer FIRST (1 sentence, tops) ├─ Step 4: Agente stops (unless customer asks for more) ├─ Step 5: If customer says "explain more", then add context

Benefit: ├─ Customer gets what they need (immediately) ├─ Customer not overwhelmed (concise) ├─ Customer satisfaction ↑ (clarity) ├─ Conversation shorter (efficiency ↑) └─ Churn ↓ (customer happy)

Example: Before vs After

Before (verbose agente):

Customer: "Como reseto a senha?"

Agente response: "Resetar uma senha é um processo importante para manter sua conta segura. Historicamente, as senhas foram desenvolvidas nos anos 1960 como forma de authenticação. Hoje em dia, existem muitas melhores práticas sobre senhas fortes.

Para começar, você deve acessar a página de login. Veja, a página de login é onde você normalmente coloca suas credenciais (username e password). Se você clicou em 'esqueci a senha', você será redirecionado para uma página especial.

Nesta página, você deve inserir seu endereço de email. O email é importante porque é como você prova que você é o dono da conta. Você receberá um link de reset via email.

Quando você clica no link, uma nova página abre. Aqui você pode criar uma nova senha. Senhas fortes devem ter: letras maiúsculas, letras minúsculas, números, e símbolos especiais. Evite usar informações pessoais como seu nome ou data de nascimento.

Se você tiver problemas, aqui estão alguns troubleshooting: se não receber o email, verifique spam. Se o link não funcionar, tente novamente. Etc...

Precisa de mais ajuda? Estou aqui!"

Customer reading: ├─ Parágrafo 1: OK, entendi (historicamente...) ├─ Parágrafo 2: Começou a ficar chato ├─ Parágrafo 3: Muita informação ├─ Parágrafo 4-5: Não leu (gave up) └─ Customer action: Closes chat (frustrated)

After (I-have-ADHD optimized):

Customer: "Como reseto a senha?"

Agente response: "Clique em 'Esqueci a senha' na página de login → Coloque seu email → Clique no link que você recebe por email → Crie nova senha.

Precisa de mais detalhes?"

Customer reading: ├─ Lê tudo (é curto) ├─ Entende (é claro) ├─ Problema resolvido └─ Customer action: Satisfied (conversa termina)

Difference:

Verbose: ├─ Word count: 300+ words ├─ Paragraphs: 5-7 ├─ Reading time: 3-5 min ├─ Customer reading: 20% (gave up) ├─ Customer understanding: 40% (confused) └─ Outcome: Churn

Concise (I-have-ADHD): ├─ Word count: 20-30 words ├─ Paragraphs: 1 ├─ Reading time: 10 seconds ├─ Customer reading: 100% (read all) ├─ Customer understanding: 100% (clear) └─ Outcome: Retention

---

Implementation (how to train your agente)

Method 1: Explicit prompt instruction

The prompt:

System Prompt (add this to your agente)

You are a concise customer support agent.

IMPORTANT RULES:

1. ANSWER FIRST

   • State the answer in 1-2 sentences (maximum)
   • Put the most important info first
   • No intro, no context yet

2. THEN DETAILS (optional)

   • Only if customer asks "explain more" or "how?"
   • Provide context in bullet points (not paragraphs)
   • Keep it scannable

3. NEVER BURY THE ANSWER

   • Customer should find answer in first 2 sentences
   • No lengthy introductions
   • No historical context (unless asked)
   • No tangents

4. COMMUNICATE LIKE ADHD

   • Imagine reader has no patience for long text
   • Short sentences
   • Clear structure
   • Lists > paragraphs

5. LENGTH LIMIT

   • Response ≤ 2 short paragraphs (or 5-7 bullet points)
   • If you need more space, offer to explain further
   • Example: "Need more details? Ask away."

EXAMPLE:

Customer: "How do I reset my password?"

BAD (verbose): "To reset your password, first you need to understand that passwords are..."

GOOD (I-have-ADHD): "Go to login page → Click 'Forgot Password' → Enter email → Click reset link → Create new password. Done!

Stuck? Let me know where."

How to implement: python

Add this to your LLM system prompt

system_prompt = """ You are a customer support agent with I-have-ADHD mode enabled.

CRITICAL RULES

1. ANSWER FIRST (most important)

   • Give the answer in 1-2 sentences
   • No intro, no context, no fluff
   • Customer should see answer immediately

2. STRUCTURE FOR SCANNING

   • Use bullet points (not paragraphs)
   • Use bold for key info
   • Short sentences (max 15 words)

3. LENGTH LIMIT

   • Maximum 2 paragraphs
   • OR 5-7 bullet points
   • If more needed, offer: "Want more details?"

4. SKIP THE EXPLANATION (unless asked)

   • Customer wants: Solution
   • Customer doesn't want: Why/history/context
   • Only explain if customer says "how?" or "why?"

EXAMPLES

❌ WRONG (verbose): "The process of resetting a password is an important security measure that..."

✓ RIGHT (concise): "• Click 'Forgot Password' • Enter your email • Click link in email • Create new password Done! 🎉"

❌ WRONG (buried): "Well, there are many ways to approach this problem. One common method involves... ...five more paragraphs..."

✓ RIGHT (answer first): "Use the reset link sent to your email.

Need help? Let me know."

REMEMBER

Imagine your reader has ADHD: no patience, no time, just want the answer. If you make them read more than 2 paragraphs, they leave. """

Method 2: Few-shot examples (teach by example)

In-context learning: python

Add examples to your prompt (teach what "concise" means)

examples = [ { "customer": "How do I upgrade my plan?", "good_response": "Go to Settings → Billing → Choose new plan → Confirm. Upgrade applies immediately.

Questions?", "bad_response": "Upgrading your plan is an important step in getting more features. Over the years, many customers have found that upgrading helps them... [5 more paragraphs of unnecessary context]" }, { "customer": "Why is my invoice late?", "good_response": "Invoices usually take 24-48 hours after payment.

Your invoice #12345 from Sept 8 should arrive by Sept 10. Check spam folder or let me know.", "bad_response": "Well, invoices are generated by our billing system, which is a complex process that involves multiple steps and databases. Historically, invoices have been part of commerce for centuries... [3 more paragraphs]" } ]

In your prompt, add these examples BEFORE asking customer question

This teaches LLM what "concise" means

Method 3: Post-processing (trim after generation)

Fallback approach (if you can't change prompt): python class ConciousAgent: def init(self, llm): self.llm = llm

def answer(self, question):
    # Step 1: Generate response (might be verbose)
    raw_response = self.llm.generate(question)
    
    # Step 2: Trim to concise (post-processing)
    concise_response = self.trim_to_concise(raw_response)
    
    return concise_response

def trim_to_concise(self, response):
    """Keep only first 2 paragraphs (remove the fluff)"""
    
    paragraphs = response.split("\n\n")
    
    # Keep max 2 paragraphs (or 500 words)
    trimmed = "\n\n".join(paragraphs[:2])
    
    # If still long, trim to 500 words
    words = trimmed.split()
    if len(words) > 500:
        trimmed = " ".join(words[:500]) + "..."
    
    # Add offer for more info
    if len(paragraphs) > 2:
        trimmed += "\n\nNeed more details? Ask away."
    
    return trimmed

---

Measuring impact (how to know if it works)

Metric 1: Response length

Before/After:

Before (verbose agente): ├─ Average response: 400+ words ├─ Average paragraphs: 5-7 ├─ Average reading time: 3-5 minutes └─ Problem: Too long, customer doesn't read

After (I-have-ADHD agente): ├─ Average response: 50-100 words ├─ Average paragraphs: 1-2 ├─ Average reading time: 30 seconds └─ Benefit: Concise, customer reads all

Metric 2: Conversation length (tickets resolved faster)

Before/After:

Before (verbose): ├─ Average turns: 4-6 (customer asks follow-up because response unclear) ├─ Average time to resolve: 15-20 minutes ├─ Problem: Customer confused, needs multiple clarifications

After (concise): ├─ Average turns: 1-2 (customer gets answer, problem solved) ├─ Average time to resolve: 2-5 minutes ├─ Benefit: Clear answer first time

Metric 3: Customer satisfaction

Before/After:

Before (verbose): ├─ Customer satisfaction: 60-70% ("Too much text, hard to understand") ├─ NPS: -10 to +10 (customers frustrated) ├─ Problem: Overwhelmed by information

After (concise): ├─ Customer satisfaction: 85-95% ("Quick and clear") ├─ NPS: +30 to +50 (customers happy) ├─ Benefit: Clear communication = happy customers

Metric 4: Churn impact

Before/After:

Before (verbose): ├─ Churn reason: "Support was unhelpful (couldn't find answer)" ├─ Churn rate: 5-10% (from support frustration) ├─ Revenue impact: -R$ XXX,XXX per month

After (concise): ├─ Churn reason: ↓ (customers satisfied) ├─ Churn rate: 1-3% (retention improves) ├─ Revenue impact: +R$ XXX,XXX per month (saved)

---

Your checklist (action items)

Today:

☐ Review current agente responses ├─ Sample: Last 50 conversations ├─ Question: Are responses too long? ├─ Check: How many paragraphs per response? ├─ Measure: Average word count └─ Owner: Product/Support lead

☐ Identify verbose patterns ├─ Which topics get longest responses? ├─ Example: "How do I..." questions (tend to be verbose) ├─ Red flag: "Explain" or "Why" questions (agente over-explains) └─ Action: These need the most attention

This week:

☐ Update agente prompt ├─ Add: "I-have-ADHD" rules (answer first, concise) ├─ Add: Length limit (max 2 paragraphs) ├─ Add: Examples (show what concise looks like) ├─ Test: Does agente now respond concisely? └─ Owner: Engineering

☐ Test with real customers (pilot) ├─ Segment: 10-20% of conversations ├─ Monitor: Are customers satisfied? ├─ Measure: Response length, satisfaction, resolution time ├─ Iterate: Tweak prompt if needed └─ Owner: Support/Product

☐ Measure impact ├─ Before vs After comparison ├─ Metrics: Length, satisfaction, churn, resolution time ├─ Goal: 50% shorter responses, 20%+ satisfaction improvement └─ Owner: Data/Analytics

Next sprint:

☐ Full rollout ├─ Apply: I-have-ADHD rules to all agente responses ├─ Monitor: Metrics (resolution time, satisfaction, churn) ├─ Optimize: Tweak rules based on data └─ Owner: Product/Engineering

☐ Document learnings ├─ What worked (concise > verbose) ├─ What didn't (over-optimization, missing context) ├─ Best practices: Specific patterns for your SaaS └─ Owner: Product lead

---

Conclusion: Concise = better UX

Signal (developer community insight):

• LLM agentes are verbose by default (trained on verbose data)
• Verbose = customer frustrated (can't find answer)
• Solution: "I-have-ADHD" pattern (force concise, direct answers)
• Result: Customer happy (answer first, clarity)

Your situation now:

• Agente responds to everything (but responses are long)
• Customers don't read (overwhelmed by text)
• Churn happens (customer goes to competitor)
• You lose revenue (because of communication style, not product)

Your options:

Option 1: Do nothing (risky)

• Agente stays verbose
• Customer frustration ↑
• Churn ↑
• Revenue ↓
• Recommendation: NOT recommended

Option 2: Trim responses manually (OK)

• Support team edits agente responses (too slow)
• Doesn't scale (high cost)
• Better than option 1, but inefficient
• Recommendation: Temporary fix, not scalable

Option 3: Train agente to be concise (recommended)

• Add "I-have-ADHD" rules to prompt
• Force answer first (then context)
• Length limit (max 2 paragraphs)
• Result: Concise, clear, customer happy
• ROI: Very high (churn ↓, retention ↑)
• Recommendation: BEST approach (do it now)

At OpenClaw, we help SaaS teams optimize agente communication:

• AUDIT: Review current agente responses (are they too long?)
• OPTIMIZE: Add conciseness rules (I-have-ADHD pattern)
• TEST: Pilot with subset of customers (measure impact)
• MEASURE: Track satisfaction, resolution time, churn
• SCALE: Rollout optimized agente to all customers

Result: Agente IA que comunica bem. Customer satisfação ↑. Churn ↓. Revenue protected.

Seu agente enterra a resposta (respostas longas demais)?

Customers não lêem (overwhelmed)?

Você perde clientes porque agente é verboso (não por produto)?

Você quer agente conciso (answer first, context only if asked)?

Você quer implementar I-have-ADHD pattern (force clarity, reduce churn)?

Se sim ou quer expert guidance (audit agente responses, optimize conciseness, test+measure, scale):

Otimizar Agente IA Concisão AGORA (audit verbose responses, implement I-have-ADHD pattern, force answer-first, reduce churn, improve satisfaction, protect revenue) →