Consulting — ABOS Architecture
Your AI agents work in demos.
They fail in production.
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 — a public registry of 16 interconnected projects spanning marketing, intelligence, publishing, and identity — built solo with AI in 27 months, starting from zero coding knowledge. Every claim on this page links to something you can inspect.
Inspect the proof first
Don't take the pitch — take the evidence. All of it is public.
Live GitHub Metrics
Commit activity synced daily from GitHub by cron. 89 active repositories in the last 30 days. Not a screenshot — a live feed.
Case Studies
The AI Orchestra Method, the Teneo build, the custody-battle AI system — documented with the failures left in.
The Projects
16 registry projects, 10 deployed, one shared auth layer, AI-to-AI service protocols between systems.
Independent Codebase Audit
March 2026: a Claude instance audited the Teneo production repo — 155+ Lambda functions, 7 domain stacks. Verdict: "needs hardening, not rebuilding."
What an ABOS Architect does
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?
Engagements
ABOS Audit
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.
- ·Evaluation pipeline review — how do you know any of this works?
- ·Failure-mode map: where agents fail and what happens next
- ·Context architecture gaps and a concrete fix plan
Agent Infrastructure Sprint
Build the infrastructure that makes agents reliable: specification systems, multi-agent decomposition, context architecture, and an eval pipeline that catches regressions before users do.
- ·Specification system your agents can actually follow
- ·Multi-agent decomposition with approval gates and kill switches
- ·Eval pipeline: LLM-as-judge, calibration sets, drift detection
Strategic Advisory
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.
- ·Async-first: documents and working sessions, not status meetings
- ·Architecture reviews on real code, not slideware
- ·AI Orchestra Method training for you or your team
Good fit / bad fit
Worth a call if
- · You've raised and your agent pilots aren't becoming production systems
- · You need eval infrastructure, not another demo
- · You want your team faster with AI, without losing control of quality
- · You can handle direct communication and ship-first iteration
Not a fit if
- · You want someone to build your whole product for you
- · You value credentials over inspectable work
- · You're building surveillance or exploitation systems
- · You want a generic "AI strategy" deck
Request a discovery call
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