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Coffee With Developers • Apr 27, 2026

Building Agents Securely at Scale - Alfonso Graziano

Alfonso Graziano

Alfonso Graziano warns that deploying AI agents blindly is a security nightmare. Stop relying on simplistic tutorials. Learn to implement hard guardrails and continuous evaluations for production-ready LLMs.

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#1 about 2 min

Building client-facing AI agents for engineering teams

Practical implementations of AI agents focus primarily on serving internal engineering teams and end customers.

#2 about 3 min

Moving beyond simple prompts to reliable agentic systems

Constructing robust AI agents requires recognizing that natural language interfaces must handle unpredictable user queries effectively.

#3 about 2 min

Why simplistic AI agent tutorials fail in production

Most introductory guides ignore critical components like automated evaluations, golden datasets, and continuous user feedback loops.

#4 about 3 min

Implementing security guardrails and OWASP principles for agents

Applying appropriate access controls and addressing new vulnerabilities like indirect prompt injection protects non-deterministic applications.

#5 about 2 min

Essential resources for understanding agentic design and evaluation

Foundational knowledge of large language models and structured frameworks aids in building easily testable AI systems.

#6 about 4 min

Evolving developer roles into tech leads for AI agents

Software engineers must review generated outputs and confidently guide parallel agent workflows instead of blindly trusting automated code.

#7 about 4 min

Risks of granting AI agents complete system permissions

Running experimental agents on personal machines exposes sensitive credentials and local files to unexpected behaviors.

#8 about 3 min

Mitigating hallucinations and sycophancy in tool-calling agents

Restricting tool access and applying framework-level guardrails helps prevent deployed agents from inventing fictitious functions.

#9 about 6 min

Building golden datasets and feedback loops for reliability

Gathering real user interactions and expert annotations forms the foundation for continuously evaluating and improving agent performance.

#10 about 2 min

Refining system prompts to eliminate specific failure modes

Adjusting domain-specific instructions within the prompt configuration significantly boosts evaluation scores and inherently prevents common errors.

#11 about 3 min

Practical enterprise use cases for automating complex workflows

Deploying intelligent agents for complex data search and reproducing repetitive development tasks safely accelerates team productivity.

#12 about 5 min

Learning resources and community engagement for AI engineers

Specialized courses, upcoming literature, and developer conferences offer structured approaches for mastering advanced software integration capabilities.

Matching moments

8:54 min

Preserving engineering fundamentals in agentic development

Chris Heilmann +1 · LIVE

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · WWC 2025

4:15 min

Security integration and AI skepticism in developer tooling

Chris Heilmann +2 · LIVE

1:10 min

Identifying emerging security vulnerabilities in generative AI agents

Alejandro Saucedo Alejandro Saucedo · WWC 2025

3:45 min

Fusing developer experience and platform engineering for agentic SDLC

Julia Kordick Julia Kordick · WWC Europe 2026

2:52 min

Designing agentic AI solutions for the enterprise

Damir Dobric · Coffee With Developers

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Upcoming sessions on this topic

Open session

World Congress 2026 North America

Closing the Visibility Gap: Lessons from Safety Critical Agentic Systems

Vivek Pandit

Principal Engineer at Cadence

Vivek Pandit
Open session

World Congress 2026 North America

Securing AI Agent Infrastructure: Identity, Attestation, and Trust at Scale

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Founder of OpenA2A

Abdel Fane
Open session

World Congress 2026 North America

When Agents Became Users: Rearchitecting Identity and Permissions for AI at Scale

Yoav Gal, Dor Cohen

Yoav Gal
Dor Cohen
Open session

World Congress 2026 North America

Who Tests the AI? Building Trustworthy AI Systems at Enterprise Scale

Him Raj Singh

PayPal, Manager, Software Engineer

Him Raj Singh
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Designing APIs That Survive AI Agents at Scale

Phani Pendurthi

Mastercard, Principal Software Engineer

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Agentic Drift: keeping pace with your agents

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