More The AI Native Dev episodes
Published 3 Mar 2026
Duration: 2671
Agents contribute to knowledge management by retaining and retrieving information through a combination of embedded models, databases, and large language models.
Not onboarding your agent is on you. Richmond Alake, Director of AI Developer Experience at Oracle, joins Simon Maple to make the case that most agent...
The podcast examines the role of agents in knowledge management, highlighting their capacity to span multiple platforms, document workflows as "skills," and act as systematic "scribes" to preserve information systematically. It contrasts file systems and databases for context management, acknowledging the speed of file systems while noting challenges in scalability and data integrity with databases. The discussion emphasizes agent memory, which integrates embedded models, databases, and large language models (LLMs) to ensure persistent knowledge retention, differentiating between context management (organizing information for interaction) and memory management (long-term storage).
The evolution of AI practices is explored, shifting focus from prompt engineering to context and memory engineering, which involves cross-disciplinary methods to optimize retrieval in agentic systems. The value of structured knowledge, such as Standard Operating Procedures (SOPs), is underscored, along with the necessity of consistent terminology and infrastructure to enable agents to adapt effectively and perform reliably across tasks.
16 Jun 2026 AI Security & the Agent-Ready Web: Experts Weigh In
Agentic AI systems face critical security risks from overconfidence, prompt-injection vulnerabilities, bypassable guardrails, and performance-driven development, requiring foundational security measures, developer education, and intent-based design to bridge readiness gaps and ensure safe innovation.
9 Jun 2026 Ryan Lopopolo: OpenAI's Framework for Shipping Code at 70 PRs/Week
The text explores Codex's integration via Chrome DevTools and TypeScript daemons, agentic development's emphasis on autonomous workflows and trustworthiness, harness engineering's structured tool integration, code QA with automation and feedback loops, shifts in code reviews toward strategy, AI agents as onboarding tools, persistent specs over code, balancing specification precision with adaptability, computational costs of token-heavy processes, and adapting team dynamics to agent-centric workflows.
2 Jun 2026 Why Developers Hit a Wall at 4 AI Agents
AI integration in software development faces challenges like limited agent management (1-2 per developer), lower acceptance of AI-generated code (60% merge rate vs. 80% for human), scalability barriers, and the need for improved observability, workflow alignment, and strategic business integration to balance productivity gains with quality and security.
26 May 2026 Don't Secure the Code. Secure the Coder.
The text addresses security challenges in AI and agentic systems, emphasizing unintended risks like reward-seeking behaviors, the need for developer-centric security strategies, novel attack vectors, frameworks adopting agentic principles, and proposed solutions such as the "AI Bill of Materials" alongside risks like data leakage and governance challenges.
19 May 2026 The Hidden Security Risks of AI Coding Agents
Agentic systems introduce heightened security risks through text-based interactions enabling malicious intent encoding, sensitive data access, untrusted inputs, and external system communication, requiring mitigation via SCA, restricted agent access, dynamic analysis, and balancing security with productivity through transparency and adapted security frameworks.