Index listing of all 14 items tagged with #orchestration.
What has to happen between a model asking for an action and that action changing something real, and which layer owns each failure.
Why much of what an action-taking AI system does comes from the software around the model, and why that software survives a change of model.
An agent harness is the software that turns a language model into an agent: the loop, the tools, the context and the limits. What each part does, with examples.
Without clear constraints, system behavior drifts into unpredictable states.
When a system fails to resolve intent, it must pass the full context, not just the failure.
In multi-agent systems, the handoff protocol is the most critical design element.
Useful AI behavior usually comes from composition across models, memory, tools, and runtime checks.
Reliable execution starts when reasoning and action are separated.
If a run cannot be explained, it cannot be trusted at scale.
Completion claims are weak until the system proves the result.
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.
How I built a policy-governed MCP runtime where models can reason freely but execution stays deterministic, verifiable, and auditable.
A walk through agent orchestration patterns, handoff protocols, and building reliable multi-agent systems.
A systems deck on assembling intelligence from models, tools, memory, and runtime discipline.