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Part 3[link] built the harness around the agent.
Retry and fallback kept one provider error from ending a run. A model router picked the model, and hard limits capped model and tool calls. Guardrails redacted PII, fenced injected instructions, and screened the request with Jev.
A permission matrix enforced roles at the tool boundary, and an approval interrup...
Part 2 gave the support agent a context engine. Each call assembled its prompt from the tenant, environment, and role.
The middleware removed stale tool results from the model's view, moved large outputs to a store, an...
When an agent product integrates one harness directly, its backend starts depending on that harness’s task format, event stream, session model, and filesystem behavior.
Unlike model routing, adding another harness is not a config/parameter change because it adds another runtime that plans work, calls tools, manages files, and decides when a task is complete.
And ...
A dense transformer applies the same feed-forward network to every token. Batching changes the input matrix dimensions, but it does not change which weights execute.
An MoE layer replaces that feed-forward network with multiple experts. A router scores those experts for each token. Only the selected experts execute for that token.
This reduces executed expert com...
At the end of Part 1, the support agent could answer a question with evidence.
It had four read tools scoped to a tenant. Its Answer schema forced every claim to carry evidence. Runtime context told tools who was askin...