Consulting — ABOS Architecture
I build the infrastructure that makes autonomous systems reliable: evaluation pipelines that catch silent failures, context architecture that gives agents the right information at the right time, and orchestration that coordinates multiple agents without cascading errors.
Not theory. I run an autonomous business operating system of 55+ interconnected projects — marketing, intelligence, publishing, identity — built solo with AI in 27 months, starting from zero coding knowledge. Every claim on this page links to something you can inspect.
Don't take the pitch — take the evidence. All of it is public.
Commit activity synced daily from GitHub by cron. 89 active repositories in the last 30 days. Not a screenshot — a live feed.
The AI Orchestra Method, the Teneo build, the custody-battle AI system — documented with the failures left in.
55+ interconnected projects, 14 deployed apps, one shared auth layer, AI-to-AI service protocols between systems.
March 2026: a Claude instance audited the Teneo production repo — 155+ Lambda functions, 7 domain stacks. Verdict: "needs hardening, not rebuilding."
Most AI consulting teaches prompts. The actual bottleneck is coordination: integrating agents into real workflows, deciding what to automate, handling the moment an agent fails mid-task, and keeping humans in control of what ships.
I've solved that coordination problem across my own ecosystem — hierarchical multi-agent orchestration with approval gates, eval-driven development with LLM-as-judge and calibration sets, context architecture that routes agents to the right knowledge across 55 codebases, circuit breakers and dead-letter queues for when things break anyway. The first question I'll ask you is the one that matters: can you show me how you measure whether any of this actually works?
I evaluate your AI agents the way I evaluate mine: find the silent failure modes, map trust boundaries, identify the context gaps that make agents guess instead of know.
Build the infrastructure that makes agents reliable: specification systems, multi-agent decomposition, context architecture, and an eval pipeline that catches regressions before users do.
For founders who want to personally operate at AI-orchestra level — direction-setting, architecture reviews, and the working methods behind 18–24 concurrent Claude instances.
Tell me what you're building and where it breaks. You'll get a direct reply within 24 hours — from me, not a funnel.
Prefer email? travis@traviseric.com