#orchestration
orchestration shows up across 5 section(s) and 24 page(s) in this workspace. Use this page as a topic map, not just an archive.
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- Systems 13 page(s)
- Sentences 5 page(s)
- Self 1 page(s)
- Shelf 3 page(s)
- Sticky Notes 2 page(s)
AI Agents vs AI Workflows
A practical explanation of the difference between autonomous-seeming agents and controlled workflows, and why the distinction matters in production systems.
Agent Instructions and Handoff as an Operating System
A practical architecture for running AI agents reliably using instruction contracts, handoff memory, and measurable quality gates.
Decision-Making Under Uncertainty in AI Runtimes
A practical framework for making accountable decisions in AI systems when evidence is partial, time is limited, and outcomes are high-impact.
AI Architecture Explained: How Modern LLM Applications Work
A practical map of the layers that make modern LLM applications reliable: model access, retrieval, orchestration, interfaces, and governance.
Designing Reusable AI Skills
How to design AI skills with clear boundaries, input and output contracts, tool limits, side-effect controls, and escalation paths.
From Agent Intent to Governed Execution
How an agent request becomes controlled system behavior through runtime orchestration, policy gates, verification, and traceability.
Knowledge Management as Runtime Memory
Why modern AI teams should treat knowledge management as a live runtime memory system, not a static documentation archive.
Observability First: How AI Systems Learn After Launch
Why observability is the missing layer between model output and reliable product behavior in production AI systems.
Runtime Over Model: Why Orchestration Is the Product
Reliable agents come from controlled execution loops, not model capability alone.
Skills vs Prompts vs Agents
A systems-level comparison of prompts, skills, workflows, and agents so teams can stop mixing up instruction surfaces with execution architecture.
The Intelligence Assembly Model
Why useful AI behavior comes from how models, memory, tools, policies, and feedback loops are assembled into one system.
What a Skill Is in AI Systems
A practical definition of skills as reusable execution units that sit between prompts and workflows in modern AI systems.
Agentic Orchestration: Designing Multi-Agent Coordination
How to design reliable multi-agent systems with proper handoff protocols, coordination patterns, and failure handling that keeps orchestration from becoming orchestration chaos.
Intelligence is assembled.
Model proposes, runtime decides.
Trace before trust.
Verification turns output into evidence.
Handoffs are load-bearing.
What I Learned Debugging a Multi-Agent System
The debugging session that taught me why observability is not optional in orchestration, and what I now look for first when a multi-agent system misbehaves.
Soothsayer MCP kernel: from prompts to controlled orchestration
How I built a policy-governed MCP runtime where models can reason freely but execution stays deterministic, verifiable, and auditable.
The Intelligence Assembly deck
A systems deck on assembling intelligence from models, tools, memory, and runtime discipline.
DAX Agentic Orchestration deck
A deep dive into agent orchestration patterns, handoff protocols, and building reliable multi-agent systems.