Agent engineering, end to end.
Four services that take work off your team and keep you in control — from a single workflow to a self-hosted model stack.
Agent Workflows
Multi-step pipelines that complete real business processes.
We decompose a business process into discrete nodes — reasoning, tool calls, lookups, validation, human approval — and model it as a stateful graph with branching, retries, and persisted state. Not prompt-tinkering: a versioned, monitored, testable workflow.
What you get
- Map a process end to end, then automate it step by step with tool calls, retrieval, and human checkpoints.
- Connect to your CRMs, ticketing, databases, and internal APIs.
- Built-in evals and tracing on every run.
- Guardrails and fallbacks at each step, so a failed call never becomes a silent error.
How we build it
Proof we show
Shipped pipeline diagrams, eval scorecards (task success, tool-call accuracy), and before/after cycle-time — backed by LangSmith traces.
- Intakeclassify & validate the request
- Plandecompose into discrete steps
- Tool callsCRMs, ticketing, DBs, internal APIs
- Validateevals, schema & guardrail checks
- Human approvalcheckpoint before any write
- Deliverwrite back + audit log
Agent Automation
Autonomous agents that take repetitive work off your team.
An always-on perceive–reason–act–remember loop. The agent watches a trigger, reasons, acts through tools and APIs, remembers across runs, and escalates only on exceptions. Most of the work is reliability engineering: retries, idempotency, fallbacks, and monitoring.
What you get
- Hand high-volume, rules-heavy tasks to an agent that runs on a schedule or event.
- Scoped permissions and action gating on everything it can do.
- Clear escalation to a human, with full context.
- Audit logs and metrics, so you can measure hours saved and errors avoided.
How we build it
Proof we show
A bounded agent with a defined action space and escalation policy, deflection metrics, guardrail docs, and an incident/fallback runbook.
[09:14:02] trigger invoice.received #4471
[09:14:03] plan 3 steps · confidence 0.92
[09:14:04] tool erp.lookup_po("PO-8832") ok
[09:14:05] validate amount within tolerance ok
[09:14:05] act erp.post_payment() ok
[09:14:06] done autonomous · 0 escalations
Hermes & Claude Code Setup
Agentic dev tooling, installed and tuned for your engineers.
Two complementary engagements. We set up Claude Code as a governed team capability — project context files, purpose-built subagents, MCP connections, and lifecycle hooks that enforce review and gate dangerous commands. And we deploy Nous Hermes open models as a self-hostable, steerable alternative you control.
What you get
- Install and configure the Claude Code CLI and Agent SDK against your repos, CI, and review process.
- Deploy and tune open Hermes models for code, agents, and internal tooling.
- Write project rules, hooks, and custom tools that follow your conventions.
- Onboard your team with patterns and guardrails for safe, reviewable agent-assisted development.
How we build it
Proof we show
A reference team config — shared plugin, subagents, MCP, and command-gating + auto-test hooks — plus a self-hosted Hermes deployment with throughput and latency numbers.
// gate dangerous commands, auto-test on edits
{
"hooks": {
"PreToolUse": [
{ "matcher": "Bash",
"command": "guard --deny rm-rf,force-push" }
],
"PostToolUse": [
{ "matcher": "Edit", "command": "run-tests --changed" }
]
}
}
Local Model Deployment
Open-source LLMs, self-hosted on your infrastructure.
We self-host open models on your hardware or in your VPC so no data leaves your network: model selection and right-sizing, the serving engine (vLLM for throughput, Ollama and llama.cpp for edge), quantization to fit the hardware budget, and an OpenAI-compatible API so existing code switches with a one-line change.
What you get
- Select and size the right open model for your task, latency, and hardware budget.
- Deploy on-prem or in your VPC with quantization, batching, and GPU tuning.
- Keep regulated or proprietary data inside your perimeter — no third-party API calls.
- Hand off a documented, monitored stack your team can operate and scale.
How we build it
Proof we show
A benchmarked vLLM cluster (tokens/sec and latency on named GPUs), a self-host vs cloud-API cost model, and an in-VPC / air-gapped architecture.
# serve an open model on your GPUs, OpenAI-compatible
$ vllm serve hermes-4-70b \
--tensor-parallel-size 4 \
--quantization awq \
--max-model-len 32768
INFO Started server on http://0.0.0.0:8000/v1
INFO Throughput: 3,140 tok/s · TTFT 180ms
We start with the process, not the model.
Scope a real workflow, build it in your stack, prove it with evals, then hand it over. The five steps below are the whole engagement.
- 01
Assess
We map one real process end to end and find where agents beat both humans and rigid RPA.
- 02
Pilot
We build a bounded pilot in your stack and run it in shadow mode against real data.
- 03
Build
We harden it: guardrails, evals, tracing, fallbacks, and idempotency before any write access.
- 04
Deploy
We ship to production with monitoring, audit logs, and scoped permissions on every action.
- 05
Operate / Hand off
We document the stack and hand it to your team — no black boxes, no lock-in.
Tell us the process. We'll show you the agent.
Bring one repetitive workflow that costs your team hours every week. We'll scope what an agent can take off your plate — and what it'd take to ship it.