Your agent will never make the same mistake again.
Every great hire got feedback. Most agents get abandoned instead.
Everyone is fighting over the 15%.
When Ajay Banga became CEO of Mastercard, he noticed the company's slogan called it the heart of commerce. Yet inside the building, everyone talked about Visa and American Express, rivals competing for the small share of payments that were already electronic.
Almost no one talked about the real competitor: cash, which at the time handled more than 85% of consumer transactions worldwide.¹
Mastercard stopped fighting over the 15% and went after the 85%.
FIG. 01 — THE REAL COMPETITOR WAS NEVER VISA.AI agents are in the same spot today.
In the US, the most AI-native market on earth, 64% of adults use AI, a number that barely moved from 61% a year earlier.² Only about 24% of AI users use an agent regularly.³ That means roughly 15% of American adults rely on AI agents.⁴ Worldwide, consumer AI reaches about 24% of the population, so roughly 3 in 4 people don't use AI at all.²
The industry is building evals, frameworks and dashboards for the 15% who already use agents. The bigger market is everyone who tried once, got stuck, and never came back.
¹ Carolyn Dewar, Scott Keller and Vikram Malhotra, CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest (Scribner, 2022). Figures as reported in the book for that historical period.
² Menlo Ventures, 2026: The State of Consumer AI, survey of 5,067 US adults conducted with Morning Consult, July 2026. menlovc.com/perspective/2026-the-state-of-consumer-ai
³ Same Menlo Ventures report, as compiled by Digital Applied, "What People Let AI Agents Access: 2026 Survey Numbers" (Sept 2026).
⁴ itera.ai estimate: 64% of US adults using AI × 24% of AI users using an agent regularly ≈ 15%.
Users don't quit because AI is bad. They quit because nobody fixed it.
Every team shipping AI agents runs into the same pattern:
Misreads one message.
Gives up.
Notices, but can't edit a prompt.
Can fix the prompt, but never sees the conversation.
One loop, from bad reply to better agent.
Chat with your agent (receptionist, sales, support) right inside itera. Every conversation is real data.
Every turn becomes a trace: input, output, prompt version, model, latency. Click any log to open the full conversation.
Select any message, mark it 👍 or 👎, and explain what should have happened, in plain language. No prompt engineering required.
Feedback goes into a self-improvement queue. itera consolidates it and runs a prompt optimizer (GEPA-style) against the exact version that served that user.
The new version runs a battery of synthetic conversations. You review the diff, see why each change was made, and promote it from staging to production.
Hi, this is Almeida & Costa Law. How can I help you today?
Hi! Can we move my hearing prep call to Friday?
Our office is open Monday to Friday, 9am to 6pm. Is there anything else I can help with?
She asked to reschedule, not for our hours. The agent should check the calendar and offer the open Friday slots.
No black-box rewrites.
Humans stay in charge of what ships. itera does the prompt engineering.
Prompt diffs with reasons.
Every changed line links back to the feedback that caused it. No black-box rewrites.
Staging for prompts.
Nothing reaches users until a human approves it.
Synthetic test battery.
See how the new version handles real scenarios before it goes live.
Full trace history.
Every input and output, from both AI and humans, versioned and searchable.
Built for the people who see the problem.
Managers and reviewers give feedback. itera does the prompt engineering.
Agents don't need better benchmarks.
They need users who stay.
Every conversation teaches your agent something. itera makes sure it learns.
Start iteratingBuilt at Supabase Select Hackathon 2026 · San Francisco