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Agentic AI Development
Technical research on production agentic AI systems. Not theory — what you learn running agents in production, not just reading about them.
Focus: Operational patterns, cost frameworks, failure modes, trust calibration. For engineers and domain experts who build.
The Dependency Stack
These aren't parallel trends. They're layered dependencies. You need orchestration before parallel execution makes sense. You need validation before you trust outputs. You need state management before self-healing works. Miss a layer, and the ones above become unreliable.
Foundation Layer
→ Orchestration + Validation
→ Model Context Protocol (MCP)
→ Model Context Protocol (MCP)
Before you can run agents in parallel or deploy them vertically, you need coordination and composable tools.
Execution Layer
→ Parallel Execution + Cost Economics
→ State Management + Self-Healing
→ State Management + Self-Healing
Speed and reliability. Cost vs time tradeoffs. Recovery when things break.
Specialization Layer
→ Vertical Agents + Economics
Domain-specific intelligence. Only viable once foundation and execution layers are solid.
Cross-Cutting Concerns
→ Memory & Context Management
→ Human-in-the-Loop Design
→ Security
→ Human-in-the-Loop Design
→ Security
These span the entire stack. Every layer needs them.
Series
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Overview. The shift from copilot to autonomous. Dependency stack introduction. What's working vs what's struggling in production. For hybrid builders: domain experts who code.
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Verification patterns that scale. Independent work patterns, Actor-Critic, triangulation. Cost frameworks and ROI calculation. How to verify intelligently without burning your budget.
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Model Context Protocol at ScaleFoundation layer. Standardized tool interfaces for composition. Practical MCP server setup, authentication patterns, central management. Domain-specific implementations.
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Parallel Execution: Speed vs Cost TradeoffsExecution layer. Parallel isn't always better. Cost economics framework: cost per decision vs human alternative. Fan-out patterns, error recovery, ROI calculations.
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Self-Healing Pipelines: The State Management RealityExecution layer. What state were you in when it broke? Idempotency, checkpointing, graceful degradation. Observable, diagnosable, repairable architecture. Trust calibration.
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Building Vertical AI AgentsSpecialization layer. Domain-specific intelligence. When vertical pays off. Cost economics: build vs run vs human alternative vs errors. Fine-tuning vs RAG vs hybrid. Case studies.
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Agent Memory: Long-Term Knowledge AccumulationCross-cutting. Episodic, semantic, working memory patterns. Persistence strategies. Retrieval optimization. Privacy-preserving memory management. Trade-offs: speed vs accuracy vs cost.
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HITL: Escalation Patterns That WorkCross-cutting. The escalation spectrum: autonomous → approval-required → advisory-only. When and how agents escalate. Avoiding escalation fatigue. Trust calibration patterns.
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Agentic Security: Attack Surface and DefenseCross-cutting. Prompt injection, tool abuse, data leakage, chain exploitation, resource exhaustion. Defense layers. Red-teaming. Compliance considerations.