Building agents. Done by the model.
Building production agents is harder than building demos. Most agent frameworks are notebooks pretending to be services — they crash, they leak memory across sessions, they make tool calls without policy, and the only test that ever ran was the demo. Aether for agents pairs the long-horizon agent runtime with capability gating, deterministic memory, and eval-gated blue/green deploys — the substrate every Apex discipline runs on, exposed for customers to build their own agents.
What the model actually computes.
These are the workloads the AI runs in building agents — rendered live in SVG. Each panel is the geometry the model is reasoning about, the physics it solves for, and the validation it lands against measured reality.
Aether is the AI doing the building agents work.
Three things to know about the AI underneath every workload on this page. We aren't building a chat surface for your team — we are training a foundation model that plans, runs and reports.
The AI does the discipline
Not a chat interface over your engineers. A foundation model that plans the study, picks the method, runs the agents, validates against measured reality and writes the report. The same shape an experienced practitioner would follow — done by the model.
One model, your context
We don't ship a sibling model per industry. The same Aether weights serve every discipline, but the agents on top are trained against your standards, your historical data and your regulatory posture.
Reality is the regulariser
We train on simulation, instrument and design data — not scraped text. The model gets better the more reality it touches. Reality is what tells it when it's wrong.
The work, done by Aether.
These are the workloads Aether already runs in building agents. Not a wish-list. Each ships with validated benchmarks and traces a senior engineer can read end-to-end.
- 01
Long-horizon agent runtime
Agents as first-class processes with identity, audit trail, policy, memory binding and versioned configuration. Pause, persist, resume across hours, days, months.
- 02
Deterministic memory
Episodic and semantic stores with hash-linked provenance. "What did the agent know" is always answerable.
- 03
Capability-gated tools
Every tool call declares the capability it needs. Customer policy maps capabilities to authorised principals. Grants are audited.
- 04
Eval as a first-class object
Test suites that run on every commit, including replay of historical traces and synthetic adversarial cases.
- 05
Blue/green deploys
Agent versions promote behind a percentage rollout, gated by the eval suite. Rollback is one click.
- 06
Observability
Every decision, every tool call, every memory write captured in an immutable trace. Exportable as OTel, SIEM, or JSONL.
“We are building the AI that does building agents — not the one that talks about it. Aether doesn't ask your engineers to drive its solvers. It runs the work, validates the artefact and writes the memo.”
The stack today.
The typical stack in building agents — a half-dozen seven-figure renewals stitched together with internal glue code. We replace the spine; we don't touch the niches that work.
The agents in the mix.
Aether coordinates these specialist agents for building agents. Each is independently deployable, but the coordination — and the foundation model underneath — is what makes them more than a folder of scripts.
How we ship into regulated building agents.
The pieces that matter to procurement, security review and the regulator. We ship into industries where the boundary is real and the audit is unforgiving.
ITAR-clean compartments for defence and aerospace work
Export-control gating on agent capabilities by jurisdiction
Sovereign / air-gapped deployments — zero outbound traffic
Reproducible runs signed by model-version hash for design-review
Other companies shipping with us.
A slice of the customers we're allowed to name. The pattern repeats: one workload, one pilot, one comparison memo we co-author.
- Aerospace
Northwind Aerospace
Retired a $1.8M CAE renewal across two quarters.
62%wall-clock reductionRead the story - Drug discovery
Lyrebird Therapeutics
Closed the discovery loop on a fibrosis programme.
9 weekshypothesis → measured hitRead the story - Chip design
Helix Silicon
RTL to signoff on a 4nm SoC — without an incumbent-EDA renewal.
0EDA license serversRead the story
The AI is also running here.
A foundation model that serves twenty-two industries today. The same weights, different agents, different pilot patterns. Browse the ones nearest yours.
Questions about building agents.
What does Aether replace in building agents?
The incumbent stack here — Open-source agent frameworks (notebook-grade), Vendor-locked agent platforms, Bespoke orchestration code — collapses into one model and one project file, with Aether for drug discovery doing the work under one safety story.
What outcomes should we expect?
Long-horizon (agents that pause, persist, resume); Capability-gated (tool use with audit trace); Eval-gated (blue/green deploys with rollback) — scoped to your workload and measured against your own acceptance criteria.
How does a pilot work?
Three to eight weeks against a workload and a win condition you co-author. We run alongside your incumbent stack on real cases; if we aren't better on the metric by quarter end, the rest of the quarter is on us.
Can it deploy inside our boundary?
Yes — managed, in your VPC, on-prem or fully air-gapped on Aether Cloud, with the audit trail and provenance the regulated parts of this work require.
The AI is ready for building agents.
Send us the workload that hurts. We'll come back with a scoped pilot — three to eight weeks, win condition co-authored. If we aren't better on the metric by quarter end, the rest of the quarter is on us.