BackContext is live: one context layer for every agent you run.
Explore BackContextTraining data for a company-specific model does not need a labeling project. Your team and its agents produce it every day. BackContext is how we capture it, connect it to outcomes, and turn it into the next version of your model.
Most teams already have logs. They are scattered across tools: Claude Code sessions, Cursor histories, Codex runs, ticket comments, the CRM. None of them record what happened next. A log tells you an agent drafted a candidate submission. It does not tell you whether the candidate got an interview. Without the outcome, a log is a transcript, not an example.
BackContext is the context layer under every agent. It has six parts:
For agents, BackContext is shared memory and safe access to keys. For Applied Crew, the journal is also the data engine.
Every recorded session goes through the same five stages.
The most valuable part of a session is often the human correction. When a recruiter reorders an agent's shortlist, or an order clerk fixes a misread variety name, BackContext records both the before and the after. Those pairs become preference data: accepted versus rejected outputs on exactly the cases where a general model goes wrong. Preference tuning on these pairs is where much of the accuracy gap over frontier models comes from.
Skills matter too. When a team writes down a rule in a SKILL.md file, every version is kept, so we can line up each recorded session with the instructions the agent had at the time.
Across customers, BackContext has captured 665K+ work records. Weekly record growth went from 24K in August to 210K in September 2026, an 8.75× jump in one month as more teams and tools came online.
Algebra IT shows the early curve. They started in mid-September 2026. BackContext was running across the team's agent tools in a day. Five weeks later they had 64K work records and three workflows mapped for fine-tuning: intake, screening and submission. Their first model is in training now. DataFoldIT, further along, has 395K records behind two production models. Hytech Seeds has 206K behind two more.
Most tasks need a few thousand verified examples. Because BackContext captures them from work that is already happening, most customers have enough within weeks.
Your journal trains your models and nothing else. It is never pooled with other customers, never used to train shared models, and encrypted at rest. Keys in the vault are released only per policy, and every release lands in the audit trail.
Install the CLI from backcontext.com and run init. It finds the agent tools on the machine and wires them up in about two minutes. When you want those records to become a model of your own, talk to us.
