Make your AI agents cheaper, more reliable, and auditable.
SACL is a coordination & memory layer that wraps any model. Same answers — a fraction of the cost — with a built-in audit trail. Proven on public benchmarks and inside a real agent runtime.
Why agents break at scale
They get expensive
Multi-agent context cost grows roughly quadratically — every agent re-reads everyone else's text.
They fail silently
As context grows, accuracy can collapse — while the agent stays confidently wrong.
They're unauditable
No record of why the system decided what it did. Useless in regulated, high-stakes domains.
One layer. Four wins.
Cheaper
Bounded, reducer-governed memory instead of re-sending growing context.
More reliable
Deterministic conflict resolution with full provenance — never silently wrong.
Auditable
Every committed fact carries provenance you can trace.
Model-agnostic
The guarantees come from the layer, not the model — no lock-in.
Validated on public benchmarks, not ours.
Same model, official datasets and scoring, SACL-on vs SACL-off. Accuracy ties on 2 of 3 benchmarks — the durable wins are cost and auditability. Every number reproducible.
Baseline coordination cost grows ~O(N²). SACL stays ~O(N).
HotpotQA · same 30 questions
Exact-match accuracy (higher is better). SACL bars in amber.
A 15×-cheaper model + SACL nearly matches the frontier.
Haiku + SACL (0.60) lands near plain Opus (0.67) — at a fraction of the cost.
97.5% fewer tokens at 128 agents (live).
4,000-agent run: 100% accuracy, $3.88 total.
Model proposes, reducer disposes.
Agents share structured, traceable state
Instead of re-reading each other's raw text, agents commit structured findings to shared state.
A deterministic reducer commits and records provenance
Conflicts resolved deterministically — no LLM in the resolution loop. Every commit is logged.
The model reads a compact, bounded, auditable state
No runaway context. Same intelligence — far less to chew on.
It drops into a real agent runtime in small, reversible steps — flag off = byte-identical, with a built-in kill switch.
Run SACL on your workload.
Paid design-partner pilots — 4–8 weeks, $5k–$25k. We integrate SACL behind a flag into your agent stack and measure cost, reliability, and auditability on your own workload.
Where it fits — and where it doesn't (yet).
- Long-running agents that accumulate state
- Many agents / high contention
- Auditability in regulated, high-stakes domains
- Cost at scale (hundreds → thousands of agents)
- Short, simple tasks
- Low-contention, single-shot work
- Free-form conversational memory — in progress
- Making a weak model smart
The limits are how you know the wins are real.