Capability promise
Context policy, constraints, evals, memory, approval boundaries, cleanup loops, and product UX.
Topic dossier
Harness Engineering
The operating layer around models: context policy, constraints, evals, memory, approval boundaries, and cleanup loops.
What has to surround the model before an AI workflow becomes a product people can trust?
This cluster treats applied AI engineering as harness design: context policy, constraints, memory, evals, permissions, cleanup loops, and product surfaces working as one operating layer.
SEO intent / Founder question
What has to surround the model before an AI workflow becomes a product?
Founders researching why agent products fail after the demo and how to make them reliable.
AI ThesisMarket framing and X evidence.fireworks-skill-memoryExecution proof around persistent agent memory.AI Devtool SprintCommercial entry for one fragile workflow.
harness engineeringagent workflow reliabilitycontext engineeringagent evals
Project evidence
Pull the topic back to execution.
Memory as a recoverable working surface
fireworks-skill-memory
A memory system for coding agents that values persistence, reuse, and editorial recall over mere accumulation.
- Per-skill memory instead of one global dump
- Designed for cross-session recovery and editorial reuse
- Turns experience into reusable operating context
Session continuity without continuity theater
fireworks-sessions-saver
A checkpointing layer for coding sessions, built to survive interruption, context loss, and handoff without pretending memory never breaks.
- Checkpoint model for coding CLI tools
- Designed around interruption, recovery, and handoff
- Makes context survivable instead of merely longer