Harness-driven capability amplification
A fixed model can perform materially differently under a different cognitive and runtime architecture. Our Labs program measures that gap rather than hand-waving about it.
COMMERCIAL SYSTEMS · INTELLIGENCE · INFRASTRUCTURE
Build the system behind the result.
AgencyXcelerate works where performance depends on more than a campaign, a tool or a single team. We design, repair and operate the commercial, technical and intelligence systems underneath growth.
THE COMPANY
Six disciplines.
One operating standard.
Different problems need different specialists. We keep the disciplines separate enough to stay credible, but connected enough to solve the whole system.
01 / CONSULTING↗
Performance
Commercial diagnostics, growth economics, sales systems, operating models, turnaround and executive decision support.
02 / LABS↗Research
Model-to-agent compilation, harness evolution, ARC-AGI-3, agent systems and externally verified capability research.
03 / SECURITY↘Security
AI and agent isolation, infrastructure hardening, secrets, access boundaries, defensive architecture and recovery design.
04 / DEVELOPMENT↘Development
Agent products, internal platforms, cloud applications, data systems, automation and production software.
05 / SYSTEMS & SETUPS↘Infrastructure
Cloud, model fabrics, analytics, tracking, integrations, deployment pipelines and the systems that make operations dependable.
06 / SUPPORT↘Operate
Ongoing optimization, incident response, performance review, implementation support and systems maintenance.
EVIDENCE BEFORE THEATRE
We publish what can be checked.
Results are separated from ambition. Internal evidence is labelled internal. External results are attributed. Research claims are versioned when the evidence changes.
Source → baseline → intervention → measured change
Case work is documented as a chain of evidence. If the baseline or measurement window cannot be verified, it does not become a performance claim.
AX LABS
The model is not the whole agent. The architecture around it matters.
HOW WE WORK
No theatre layer.
Good systems work tends to look less dramatic than bad systems work. It has clear constraints, clean interfaces, explicit owners and evidence that survives scrutiny.
01
Find the real constraint
Do not optimize the visible symptom when the failure lives somewhere else in the system.
02
Design for operation
A clever architecture that nobody can run, inspect or recover is not a finished system.
03
Separate proof from promise
Measured results, working hypotheses and future targets should never be written as the same thing.
04
Make ownership explicit
Every critical state, metric, task and failure mode needs a named owner or machine-enforced rule.
Security is part of the architecture.
Agent systems create new attack surfaces: tool access, secrets, browser state, provider keys, execution sandboxes and mutable workflows. We design boundaries before the incident, not after it.
Development that survives contact with production.
We build systems people actually have to run: internal products, customer software, AI agents, automation, integrations and the infrastructure around them. Reliability, reversibility and observability are part of the definition of done.
Setups become systems.
Analytics, cloud, model routing, deployment, CRM, data, tracking and automation should not exist as a pile of accounts. We turn them into an operating layer with standards and ownership.
Stay with the system after launch.
Important systems change. Models disappear. APIs break. Markets move. We support the operating layer after deployment, measure degradation and repair what no longer works.