Try for free
Tech Unboxed
Tech Unboxed

Why Platform Maturity Determines Agentic AI Success

21 August 2026 22:31 BBD Software

Listen to episode

About this episode

AI is getting more capable, but it is also getting more expensive and that changes everything. We sit down with Jason Perry, a cloud and platform engineer at BBD Software, to talk about agentic AI that actually survives contact with real enterprise workflows: budgets, governance, security, compliance, and the uncomfortable question of responsibility when autonomous agents act on their own. 

We dig into why “strategic deployment” beats chasing the largest model, and how rising token costs are pushing organisations to prove ROI in concrete terms. Jason breaks down what makes AI platform engineering different from simply using off-the-shelf productivity tools, then walks through the biggest blockers teams hit today. We cover cost control guardrails, observability, the EU AI Act and other evolving regulations, and why agent identity and access management is often the most technically complex piece of scaling agentic AI safely. 

We also explore practical ways to reduce waste while improving quality: minimising tokens per correct answer, narrowing context, limiting tools, and using techniques like semantic routing and model response caching through an AI gateway. Finally, we look ahead to the shift toward small language models and how that can reshape cloud versus on-prem decisions around data sovereignty and governance. 

If you’re building an AI strategy, an AI platform, or autonomous agents you can trust, subscribe, share this with a teammate, and leave a review. What is the one workflow you would automate with agentic AI first?

More AI podcast episodes

Browse all →

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Tech Unboxed. 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