deeda Think — developer docs

ELI5: four dials control spend. Budgets cap a run. Caching makes repeated context nearly free. Batching trades latency for ~50% off. Headroom compresses context before the provider ever sees it.

Budgets (always set these)

budget:
  tokens: 100000
  wall_clock_minutes: 45

Cadence enforces both; a run that hits its budget stops cleanly and the session ledger records actual usage and cost per event.

Prompt caching

cache_policy:
  enabled: true
  strategy: auto
  key_scope: workflow   # workflow | ticket | global
  ttl: 1h

auto lets the harness place provider cache breakpoints (Anthropic ephemeral cache blocks, Gemini cached content, OpenAI prompt caching) — you choose scope and TTL, not provider mechanics. Cache scoping is workspace-isolated on the vendor side.

Batching

Batch APIs are implemented for Anthropic (anthropic.batches.create) and the OpenAI batch surface — right for evals, backfills, and bulk classification where minutes of latency are fine for ~half price. Drive them through a workflow stage that submits and a later stage (or trigger) that collects.

Headroom (context compression)

harness_config:
  sdk_settings:
    anthropic:
      headroom:
        enabled: true
        token_budget: 120000

Headroom runs before the provider call, compresses context to the budget, and emits evidence either way (headroom.compression / headroom.skipped) — you can audit exactly what it did.

When to use what

Symptom	Dial
Same big system context every turn	`cache_policy`
Long-running agent blowing context windows	Headroom
Thousands of independent items	Batch
Runaway spend risk	Tighter `budget` + `retry.max_attempts`

See also

Workflow Schema — field shapes
Provider pages: Anthropic batch/cache rows