Try for free
How to Make Agentic Adversarial Review Actually Adversarial
Event PAST

How to Make Agentic Adversarial Review Actually Adversarial

13 August Online

Join this event

🎟️ Register / View Event Details

About this event

Details

By now it’s standard advice, in the research and in practice: don’t let a model grade its own homework, bring in a second one to review it. It does find things. But it reviews inside the frame you handed it, and if it can detect what answer you’re hoping for, it tends to find its way there.

This is a walkthrough of a real Claude Code conversation where both of those bit me on my own work, and what I did about each. By that point Carmen Mardiros had a specific understanding of how sycophancy, autoregressive commitment, and recency bias behave, and used it to steer the conversation while it was running. That steering is most of what I want to show, so that other people can leverage the same mechanisms. It may change how you run reviews with the current generation of frontier models.

What you’ll leave with:
🎯 What to withhold from a reviewing agent so it challenges the premise rather than the detail
🎯 What to say to an agent that’s already agreed with you without triggering over-correction in the other direction
🎯 How to ask for evaluation, opinion and conclusions without leaking your implied bias to the model.

🗓️ Save the Date: 13th August, 2026
📍 Location: Sahaj Software, 1 Quality Court, London, WC2A 1HR
🕕 Time: 6:00 PM – 8:00 PM (BST)

The evening will feature:

🎤 One engaging talk with Q&A
🤝 Plenty of time for networking over free pizza and non-alcoholic drinks
Don’t miss this chance to learn, connect, and share with fellow tech enthusiasts.
👉 Please click the link below to register.
https://sahaj.ai/events/how-to-make-agentic-adversarial-review-actually-adversarial/

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 DevFound.ai. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

Still have questions?

Contact Support