How do you optimize for AI agents that complete tasks vs just answering questions? Different strategy needed?

You’re ahead of the curve but not too early. This is real and coming fast.

The distinction matters:

Conversational AI (current GEO):

• User asks question
• AI synthesizes answer
• AI cites sources
• User takes action

Agentic AI (emerging):

• User describes goal
• Agent researches options
• Agent evaluates and selects
• Agent executes (or presents shortlist)

Why this changes optimization:

For conversational AI: “Be worth citing” For agentic AI: “Be worth selecting and using”

What agents evaluate:

Agents need to programmatically understand your service, not just read about it.

We’re already seeing agent-like traffic patterns:

What we’ve noticed:

• Rapid sequential page visits (not human browsing patterns)
• Service pages hit with pricing and spec pages
• API documentation getting more bot traffic
• User agents we don’t recognize

We think these are:

• Research agents gathering vendor information
• Comparison tools building databases
• Early agentic systems evaluating options

Traffic is small but growing:

• 6 months ago: ~100 visits/month with these patterns
• Now: ~800 visits/month
• Conversion rate: Lower than human, but not zero

The implication: Agents are already researching. If your information isn’t structured for them, they skip you. If it is, you make their shortlist.

This isn’t future - it’s happening now at early scale.

Interesting. So if agents are already researching, what should we prioritize?

Our business is B2B SaaS. What would agent optimization look like for us specifically?

For B2B SaaS specifically:

Priority 1: Pricing and packaging clarity

Agents need to compare options. If your pricing is “contact us,” you’re invisible to comparison agents.

Do:

• Clear pricing tiers on website
• Feature comparison tables
• Machine-readable pricing (schema markup)

Priority 2: Integration documentation

Agents evaluate “can this work with what we have?”

Do:

• Clear integration list
• API documentation
• Technical requirements
• Implementation timelines

Priority 3: Service definition schema

{
  "@type": "SoftwareApplication",
  "name": "Your SaaS Product",
  "applicationCategory": "BusinessApplication",
  "offers": {
    "@type": "Offer",
    "price": "99.00",
    "priceCurrency": "USD",
    "priceValidUntil": "2026-12-31"
  },
  "operatingSystem": "Cloud/Web",
  "softwareRequirements": "Modern web browser"
}

Priority 4: Proof points

Agents weigh reputation signals:

• Customer count
• Review ratings
• Case study outcomes
• Compliance certifications

Make these machine-readable, not just human-readable.

Hot take: The companies that win with agents will be ones with APIs.

Why:

Agentic AI doesn’t just research - it executes. If an agent can:

1. Check availability via API
2. Compare pricing via API
3. Book/purchase via API
4. Check status via API

You become the path of least resistance.

Example: User to agent: “Book me a hotel in SF under $300 with good reviews”

Agent evaluates hotels. Some have:

• API access
• Real-time availability
• Programmatic booking

Others require human intervention to book.

Which does the agent prefer?

For B2B SaaS:

• Self-serve trial signup
• API for account setup
• Programmatic pricing/quoting
• Integration APIs

These aren’t just nice-to-have for humans - they’re essential for agent interactions.

This is crystallizing. Here’s what I’m taking away:

For current GEO (conversational AI):

• Content citation
• Authority signals
• Answer-first structure
• E-E-A-T

For future agentic optimization (emerging):

• Structured data about services/products
• Machine-readable pricing
• API accessibility
• Clear documentation
• Programmatic interaction capability

Our action plan:

Now (supports both):

1. Implement comprehensive schema markup
2. Create clear pricing page with structured data
3. Document integrations and requirements clearly

Soon (agent-specific):

1. Evaluate API exposure for agent interactions
2. Make more information programmatically accessible
3. Monitor for agent-like traffic patterns

Track:

1. Agent-like traffic (Am I Cited + server logs)
2. Structured data validation
3. How we appear in agent-driven comparisons

Does this framework make sense?

Quick wins you can do today for agent readiness:

1. Pricing page structure (2 hours)

• Clear pricing tiers
• Feature tables
• Product schema markup

2. Integration documentation (4 hours)

• List all integrations
• Technical requirements
• Implementation process

3. Service/Product schema (2 hours)

• SoftwareApplication schema
• Offer schema for pricing
• AggregateRating for reviews

4. FAQ schema for common agent queries (1 hour)

• “What does [product] cost?”
• “What integrations does [product] support?”
• “What are requirements for [product]?”

These help both current GEO and future agentic optimization. Low risk, potential high reward.

Perspective on timing:

2023-2024: Conversational AI dominates (ChatGPT, etc.) 2025: Agentic AI emerges (OpenAI Operator, Claude tools) 2026+: Agents become mainstream for tasks

We’re in the transition year. The companies optimizing for agents now are like those who started SEO in 2005 - early enough to establish advantage before competition.

My advice:

• Don’t ignore conversational AI (still dominant)
• Start preparing for agents (structured data, APIs)
• Monitor agent trends quarterly
• Be ready to accelerate when agents go mainstream

This is a “prepare now, accelerate later” situation. The foundation you build today for structured data helps both current and future optimization.