The Agent Reliability Layer
We run agent evals on real workflows, evaluate where models fail, then ship high quality RLHF correction traces back to your training loop.
BACKED BY
TOOLING
What the stack includes
Everything included in our stack, explained at the product level: what Archal Labs serves to run, measure, produce, and what deliverables you receive from a pilot engagement.
EVALUATE
01
Benchmark your agent on real workflows and get a clear breakdown of where it slows down, gets stuck, or fails, plus what to fix next.
ARCH EVAL & ARCH ENGINE
CAPTURE
02
Collect high quality RLHF recovery traces with privacy safe redaction and consistent labeling, so every trace is ready for training and review.
ARCH BOX
CURATE
03
Turn raw traces into focused suites that improve long task reliability, correct tool use, and failure recovery across edge cases.
INTERNAL GRADE PIPELINE
DELIVER
04
Receive clean eval packs and datasets in a standard format that plugs into your stack fast, with pilot support and integration guidance.
ARCHAL LABS
ARCH BOX
Isolated golden workspaces for our talent pool to produce the highest quality training data for your agents.
Standardized environments for repeatable runs across humans and agents
Built to separate sensitive task details from exported trace data.
Used for pilots, internal eval, and scaled collection.
ARCH EVAL
Benchmark runner for live agents with ground truth, OSWorld-style checks, and reliable scoring in real-time environments.
Runs fixed benchmark suites in batches and produces comparable metrics over time
Measures recovery behavior, tool correctness, and overall task completion.
Automatic routing of agent task failures to our talent pool for generating high-quality “gold” fixes
ARCH ENGINE
IN DEVELOPMENTUnlimited evaluation loop for tool-use capabilities when fixed benchmark suites are not enough.
Discovers new model failure test-cases automatically
Autonomously scores model outcomes with multi-pass reviews
Keeps our talent pool focused on the highest leverage fixes