step 1 of 3
Today · single-prompt chatbot
- One LLM call, no tool grounding
- No memory across sessions
- Failure surface = the model's bad day
- No audit trail, no rollback
Recoverymanual
// lore × mathematicsThe agentic
The agentic
operating system.
Lorematics designs the orchestration layer between fragile prompts and production-grade autonomous systems — multi-agent meshes, agentic commerce pipelines, deterministic LangOps guardrails.
currently openscheduling 2026 Q3 engagement scoping callstalk to the architect, not a sales rep
// research// executor// guardrail// reviewer
Orchestrated meshes, not monoliths.
Every agent in the mesh has a job description, a tool contract, and a routing policy. Watch a four-agent workflow execute end-to-end below.
topologyn=6 nodes
trace6 events
// agent trace0/7 events
- // idle · press "Run workflow"
// pillar 02 · deep enterprise integration
From fragmented silos to one agentic pipeline.
We connect the systems your business already runs on — CRM, ERP, data warehouse, payment rails — into a single autonomous commerce path.
Before · fragmented siloslegacy
- ›POST /crm/legacy/v2/contacts
- ›GET /erp/orders?status=open
- ›POST /finance/invoice/draft
- ›GET /ticketing/inbox/today
- ›POST /email/send (manual copy-paste)
After · agentic pipelinelive
- ›agent.intent → resolveContact(crm)
- ›agent.plan → reconcileOrders(erp)
- ›agent.act → draftInvoice(finance)
- ›guardrail.eval(policy=AR-2)
- ›agent.commit → notifyOwner(email)
// every API stays. only the orchestration layer changes.
// case study— VP Engineering · attribution withheld per MSA
8.2s→480ms
p95 on the support-agent path · Fortune-500 hospitality group · 1,400+ properties · 78 markets · 22 languages
// migration · 7-step Dialogflow flow → 4-agent LangGraph topology
// beforelegacy stack
- p95 latency
- 8.2s
- fallback-to-human
- 31%
- integration surface
- 14 disconnected APIs
// afterafter lorematics
- p95 latency
- 480ms
- fallback-to-human
- 4%
- integration surface
- 1 agentic pipeline
trace.exec.0xA47Fexit 0
- intent.classify → 18ms ✓
- research.agent → 142ms ✓ (3 tools)
- guardrail.policy=AR-2 → 6ms ✓
- executor.agent → 287ms ✓
- human.review (skip) → 0ms ✓ (confidence=0.94)
- ── trace.complete · 480ms · token cost $0.0031
“We expected a chatbot upgrade. We got an orchestration layer the rest of the org now builds on.”
Deterministic safety over probabilistic chaos.
Toggle Safety Mode and replay an adversarial prompt. The guardrail evaluator intercepts in deterministic time, not on a model's good day.
policiesINJ · PII · SCOPE · RATE
modedeterministic
langops · safety simulator
// select a prompt to evaluate
From single-prompt chatbots to LangGraph topologies.
We map your existing chatbot to a multi-agent framework — LangGraph, CrewAI, AutoGen — preserving intent, eliminating brittleness.
step 2 of 3
Interim · state-machine wrapper
- Deterministic routing between intents
- Tool calls with explicit schemas
- Per-step logs and replay
- Brittle when intents collide
Recoveryscripted
step 3 of 3
Lorematics · multi-agent topology
- Specialized agents per task class
- Routing policy + guardrail per edge
- Replayable token traces, full audit
- Hot-swappable nodes, zero-downtime
Recoveryautonomous
Humans and digital agents in the same task graph.
Every task gets a lane: autonomous, assisted, or human-only. Explicit handoff topology means no silent failures.
// task ledger · digital3 mapped
- 01Triage inbound support tickets97%
- 02Reconcile mismatched invoices92%
- 03Schedule outbound follow-ups95%
// every engagement starts by mapping your task graph.
run a discovery sprint →// engage
Talk to a senior engineer.
No SDR funnels. The first call is with someone who will architect the system. Bring a problem; leave with a topology.