Core Automation helps organizations adopt AI the way good engineering gets adopted — efficiently, repeatably, with results you can put a number on. We engineer the harnesses, evaluations, and infrastructure that turn a capable model into a system that delivers consistent quality.
Every engagement starts narrow. We don't sell strategy decks — we engineer the loop your AI runs in, the evals that prove it works, and the playbook your team owns once we leave. Repeatable results, not one-off demos.
Three capabilities most teams don't have time to build alone.
The pieces that make an AI system feel like infrastructure rather than a demo. We bring them with us; you keep them when we leave.
01 / Business-aware
Agents that know your business.
A generic chat model knows everything and nothing. The agents we build are grounded in your processes, vocabulary, policies, and data — so they answer like a tenured employee, not a polite stranger.
→Domain-tuned retrieval over your data & policies
→Tool calls into the systems your team uses
→House voice, refusal rules, escalation paths
→Evals from your edge cases, not benchmarks
OutcomeAnswers you'd have given
02 / Model routing
The right model, chosen at runtime.
Frontier models are expensive. Small models miss nuance. Our harnesses route each request to the right model based on the task — with a clean swap path when a better one ships.
→Per-task routing across providers & tiers
→Quality, latency & cost budgets, enforced
→Automatic fallback & retry on degradation
→One-line swap when a new model lands
simple Q&Ahaikutool reasoningsonnetdeep analysisopusinternal lookuplocal ft
Outcome~60% cost cut
03 / Agent platforms
A platform of agents, not one chatbot.
Built on our proven AgentWorkforce architecture: a fleet of specialist agents behind a triage router, reachable from any channel, extensible by skill files — no core changes required.
→Triage agent routes to focused specialists
→Persistent memory with hybrid semantic search
→Reachable via CLI, chat, webhook, cron, alerts
→Skills added as YAML — no code redeploys
OutcomeAn AI workforce, not a bot
— Architecture · AgentWorkforce
An AI workforce, built on a proven platform.
We don't start your custom agent project from a blank page. Our AgentWorkforce architecture has been running in production — a battle-tested foundation for designing fleets of intelligent agents that share memory, route work, and reach users anywhere.
Fig.01 — AgentWorkforce architecturev1 · in production
CLI
Chat
Webhook
Cron
Heartbeat
Hooks
│ │ │ │ │ │
LAYER 01
Gateway · Dispatcher + Event Bus + Sessions
A single processing path for every input, whatever the channel.
│
LAYER 02 · CORE
Multi-Agent Router · Triage + Specialists
Each agent has a focused skill set, a system prompt, an isolated history.
│
LAYER 03
Provider Bus · Three-tier model strategy
tierhaiku lookup & classify
tiersonnet triage & tool use
tieropus deep reasoning
│
LAYER 04A
Skill Engine
YAML/MD skills · inter-skill SDK
LAYER 04B
Memory
SQLite · hybrid vector + BM25
LAYER 04C
Security
OS keychain · sanitizer · scopes
// Same architecture, re-skinned for your domain. We bring the layers; you bring the work.
01 · Specialist agents, not a generalist
A triage agent + a fleet of experts.
Operations, research, finance, customer support — each agent has its own system prompt, skill set, and conversation history. A triage agent routes incoming work to the right one. No one model carries every responsibility, and no domain leaks into another.
02 · Skills as configuration
New capabilities ship as files.
Each skill is a markdown file with YAML frontmatter and a shell or Python handler. Adding "look up an order," "post to Slack," or "run an SOP" is editing a folder — not redeploying the system. Skills can call other skills, with explicit dependency tracking.
03 · Memory that persists & learns
Hybrid retrieval, in the loop.
Persistent memory backed by SQLite, with 70/30 hybrid search across vector embeddings and BM25 keywords. Personas, preferences, prior decisions — all retrievable, all injected into the agent's context at the right moment.
04 · Multi-channel, single brain
Reach your agents from anywhere.
CLI, chat (Telegram, Slack), email, webhooks, cron schedules, lifecycle hooks — every channel feeds the same gateway and the same agents. A request from a customer and a scheduled overnight job follow identical processing paths.
05 · Proactive, not reactive
Heartbeat & lifecycle hooks.
Agents wake up on a schedule, check the systems they own, and surface only what needs your attention. Lifecycle hooks fire on system events. The platform earns its keep while you sleep.
06 · Three-tier model strategy, by default
Cost & quality, intentional.
Each agent declares the tier it needs — Haiku for lookups, Sonnet for routing, Opus for deep reasoning. Cost trade-offs are visible in config, not buried in code. Auditable, swappable, defensible.
The loop that makes AI behave like infrastructure.
The canonical Core Automation pipeline: six stages that run automatically on every code change, every customer request, every input. Each stage is owned by an agent, instrumented by the harness, and gated by the eval suite.
01
Input arrives
Code change, customer query, document, ticket — whatever the agent is built to handle.
02
Smart routing
The right model for the task. Frontier when it earns it, small when it's enough, local when privacy demands.
03
Ground in your data
Retrieval over your policies, runbooks, schemas, and history. The agent answers with context, not platitudes.
04 / core
Reason & execute
The agent plans, calls tools, writes code, drafts answers. Every action traced; every assumption logged.
05
Verify against evals
Rubric-graded checks, refusal policy, safety guardrails. Output gates on quality, not vibes.
06
Ship & observe
Deliver to the user; log everything to the trace. Every run becomes a future eval fixture.
// Figures from a representative 12-week engagement with a 200-rep B2B platform. Your numbers will vary by codebase size and starting point.
02 — How we work
Specify. Instrument. Ship.
Every engagement runs on the same loop. The artifacts change — the discipline doesn't.
01
Brief — week 0
A 90-minute working session. We leave with a one-page memo: scope, success metric, the failure mode we'll be hunting, and what counts as done.
02
Baseline — week 1
Before we change anything, we measure. Fixtures from your real traffic, a first-pass eval suite, and an honest readout of where the system actually is.
03
Build — weeks 2 to N
We write the code. PRs land in your repo. Each change ships with the eval delta attached. No surprise architectural changes — the brief is the contract.
04
Handover — final week
A handover memo your on-call engineer can actually use: how it works, how to extend it, where it'll break, and what we'd do next. We're available for questions for 30 days after.
03 — Why us
Why teams pick Core Automation.
— 01
Senior engineers only.
No layered staffing. The person writing your harness is the person who shipped the last four. No juniors learning on your stack.
— 02
Code, not slide decks.
Our deliverables run in CI. The memo at the end is short because the work is in the repo — reviewable, testable, and yours to keep.
— 03
Built to be repeated.
The work we deliver is a recipe, not a one-off. Patterns, playbooks, and templates your team applies to the next agent, the next surface, the next quarter — without us in the room.
— 04
Fixed scope, fixed fee.
We quote the engagement up front. Scope changes are renegotiated explicitly — no scope creep, no surprise invoices, no incentive to drag.
04 — Recent work
Selected engagements.
Client names are confidential; the shape of the work isn't.
01
Harness design · 6 weeks
B2B SaaS, support automation
Replaced a brittle prompt-stack with a routed multi-agent harness. End-to-end task completion 0.54 → 0.83, with answer variance cut by 60%. 30% drop in token spend and a citation policy legal approved on first review.
2026 · Q1
02
Eval strategy · 4 weeks
Fintech, document understanding
Built fixtures from 18 months of edge-case traffic, with rubric-graded judges in CI. Caught three silent regressions in the first month — including one that would have shipped to production on a Friday. The team now ships AI changes the same way they ship code.
2025 · Q4
03
Advisory · ongoing
Series B, internal R&D platform
Embedded with the platform team building shared AI infrastructure for the rest of engineering. Weekly architecture reviews, PR reviews on the critical path. Three product teams shipped repeatable AI features on the platform in the first quarter.
2025 · ongoing
04
Harness design · 8 weeks
Healthcare, clinical triage assistant
Designed a refusal-first harness with tiered guardrails and a fully audited tool-call trail. Passed an external red-team in week 7. Zero unsafe completions across 4,000 evaluation runs — and a repeatable safety pipeline the team now applies to every new model swap.
2025 · Q3
05 — Start
Tell us what you're trying to make repeatable.
A 30-minute call. No deck. We'll tell you in the call whether we're the right fit — and if we're not, who is.