Agent Workflows

Agent workflows are clients that use opentraces as working memory. They search prior session history, progressively load relevant trace evidence, and hand a small packet to the next agent step.

This is not dataset publication by default. It is an agent using the trace bucket to orient itself.

Context Warmup

Natural-language prompt:

Find prior traces in this repo related to the current task. Start with a broad
trace query, inspect only the most relevant candidates, then load the smallest
context and Trail evidence needed to brief the next agent step. Do not publish
anything.

Commands:

opentraces trace query --cwd --lex "auth middleware regression" --json
opentraces trace map <trace-id> --bursts --json
opentraces trace slice <trace-id> --template bursts --json
opentraces ctx <trace-id>:7 --json
opentraces ctx resume <context-node-id> --json
opentraces trail track <trace-id> --json

The output is a compact working packet: the relevant intent, bounded trace slice, context resume packet, and Trail survival evidence.

Progressive Discovery

Use the cheapest read first:

1. trace query for candidates.
2. trace map --bursts for intent and edit clusters.
3. trace slice for the bounded step window.
4. ctx <trace>:<step> (or ctx resume) for model-visible context.
5. trail track for whether the trace output still survives.

The agent only loads full trace content after the candidate is clearly useful.

Session History Search

opentraces trace query --cwd --since 30d --lex "database migration" --json
opentraces trace query --skill opentraces --semantic "huggingface push failing" --json
opentraces trace get <trace-id> --bursts --json

--semantic matches a small concept dictionary (services and libraries seen in trace evidence); queries outside it fall back to all-tokens-must-match lexical search, so keep query terms concrete.

This lets an agent ask, "Have we solved something like this before?" without turning the answer into a dataset row.