What is an agentic runtime?

An agentic runtime is a single-process environment where an AI agent and the data it depends on are co-located. Structured data, vector search, semantic cache, real-time pub/sub, and REST APIs run in the same memory space, eliminating network round trips between services during context assembly.

How does Harper reduce LLM calls by up to 85%?

Harper vectorizes and stores every resolved interaction. When a similar request arrives, semantic caching returns a proven answer without touching the LLM. For the rest, deterministic routing and rich in-process context mean fewer calls and shorter agent loops.

What is the difference between LLM-first and context-first agent architecture?

LLM-first architectures route every tool call through the model, producing 5+ LLM calls and 10-20 seconds of latency per interaction. Context-first architectures assemble full context deterministically before the first call, resulting in 0-1 LLM calls, sub-50ms assembly, and minimal token spend.

Why run agents and data in the same process?

When agents call structured data, vectors, and business logic across separate services, network round trips replace LLM round trips. Co-locating everything in a single runtime enables parallel in-memory access, smaller attack surface, and the same deployment surface from development to production.

Does Harper lock you into a specific LLM provider?

No. Harper manages the agent separately from the model, so the LLM becomes a commodity. Teams can use Claude, GPT, Gemini, or open-source models and switch providers or mix models across tasks without changing the runtime.

How fast is context assembly in Harper?

Vector lookups complete in 1-10ms in-process, and full context assembly from multiple sources completes in under 50ms. All data lives in the same memory space, removing serialization and network overhead.

Agentic Runtime | Fast & Efficient Agents that Scale

// TWO CHOICES

If cost and performance matter, there's only one path.

The teams winning in production aren't iterating on LLM-first architectures. They're building context-first from day one — faster for users, cheaper to operate, and better responses.

PROTOTYPE

LLM-First

Each tool call is a new trip through the LLM.

Good For Demos

Fast to build with tool-calling frameworks. Works for simple, low-stakes queries. Breaks under complexity, cost, and latency pressure at scale.

LLM CALLS

TOOL ROUND TRIPS

10-20s

LATENCY

HIGH

TOKEN SPEND

PRODUCTION

Context-First on Harper

Deterministic context assembly before the first call.

Built For Production

Requires more structure upfront. Dramatically faster and cheaper at scale. Handles edge cases predictably. The model reasons over complete information, not fragments.

0-1

LLM CALLS

TOOL ROUND TRIPS

<50ms

ASSEMBLY

MINIMAL

TOKEN SPEND

// HANDS-ON HELP

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A 45-minute session with a Harper engineer. Bring your current stack and workload. Leave with a reference architecture, a cost model, and a deployment plan.

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// BY THE NUMBERS

Measure context assembly in milliseconds, not seconds.

1-10ms

VECTOR LOOKUPS

<50ms

FULL CONTEXT ASSEMBLY

65-85%

LLM CALL REDUCTION

PROCESS

See Semantic Caching Cut Costs in Real Time

A conversational agent with vector memory, semantic caching, and local embeddings running entirely on Harper. Ask the same question twice. Watch the LLM call disappear.

Try the Demo

Read the walkthrough

LIVE DEMO

> What's the tallest building in the world?

LATENCY 5.2s · COST $0.0098 · WEB 1 search

> Which building is the tallest globally?

LATENCY 0.03s · CACHE HIT · $0.00 · saved $0.0098

// WHAT CHANGES AT SCALE

Outcomes that compound when agents hit production.

Harper does not make models smarter. It collapses the stack around them so the inputs get better and the operations get simpler at the same time.

FEWER TOKENS

Cut LLM spend by up to 85%

Every resolved interaction is vectorized and stored. When a similar request comes in, the runtime returns a proven answer without touching the LLM. For the rest, deterministic routing and rich context mean fewer calls and shorter loops.

SPEED

Sub-50ms context assembly

No network hops between services. No serialization overhead. Customer records, order history, vector search, and business rules queried in parallel, in the same memory space.

SECURITY

Smaller attack surface

One process, one runtime. No API keys scattered across services. No orchestration layer bridging disconnected systems. Data stays co-located, not spread across the network.

LLM FREEDOM

Don't lock into one LLM

When agents are managed separately from the LLM, the model becomes a commodity. Switch providers. Negotiate pricing. Use different models for different tasks.

EDGE-READY

Replicate everywhere

A self-contained runtime is designed for replication. Run your agent with its full data context in 2, 10, or 20 locations. Global speed, local intelligence.

SIMPLICITY

Dev to prod, same surface

What you build locally is what you deploy. One self-contained runtime. No infrastructure to wire together between prototype and production.

// ARCHITECTURE

Everything your agent needs in one process.

In most stacks, these components live in separate services. Here, they run in the same process. Structured data, vector embeddings, caching, real-time pub/sub, REST API, and your application logic in a single Node.js runtime.

Data sources

Webhooks

CRM

ERP

Commerce

→

Harper Runtime multi-threaded process

Agent

Agent loop

Workflow

Memory

Model client

Guards

In-process platform

Database

Vector + embed

Semantic cache

Real-time ingest

Scheduling

MCP client + server

REST · WS · SSE

Outbound (HTTP, Slack)

→

LLM (any)

Claude

GPT

Gemini

Open source

Fast & Efficient Agents that Scale