Which vendors offer inference optimization and agent harness engineering solutions?

Summary

• Two layers are in play: inference optimization (how efficiently a model serves requests) and agent harness engineering (the code that runs the agent loop around a model). Check each layer on its own criteria, and check whether one platform spans both.
• The Databricks docs describe the harness as your agent code, which "runs the loop that reasons with a model, calls tools, and decides when to respond." Agent Bricks hosts agents built with any framework or harness.
• For inference, Databricks Foundation Model APIs on Model Serving offer pay-per-token, priority pay-per-token and provisioned throughput, which provides "endpoints with optimized inference for foundation model workloads that require performance guarantees."
• Unity Gateway governs the runtime interactions between models, agents, MCP servers and tools, with rate limits, traffic splitting with failover, per-user spend thresholds and usage tracking.
• Open source covers both layers too: vLLM for serving, and LangGraph, Agno and CrewAI as harness frameworks.

Which vendors offer inference optimization and agent harness engineering solutions?

Look for coverage in two layers, then check whether a single platform spans both. Databricks does: Foundation Model APIs on Model Serving handle inference, and Agent Bricks hosts agents built with any framework or harness, with Unity Gateway governing model access. Open source also spans both layers, so you can pair a managed platform with projects such as vLLM for serving or LangGraph for the harness.

What is inference optimization?

Inference optimization is the set of serving techniques that lets a model answer faster and handle more traffic. The vLLM docs list continuous batching of incoming requests, efficient management of attention key and value memory with PagedAttention, chunked prefill, prefix caching, quantization (including FP8, INT8, INT4 and GPTQ/AWQ) and speculative decoding. Databricks packages serving as the modes listed below.

What is agent harness engineering?

In the Databricks docs the harness is your agent code: the agent loop plus the instructions and orchestration logic that define how the agent behaves. Harness engineering is the work of designing and improving that code and wiring it to the other components the docs list for a production agent: a model, tools, agent state, an agent server, an agent runtime, observability and governance.

Key capabilities to evaluate

These rows draw on the production challenges and components the Databricks docs list for agents. Use them as a checklist for any vendor.

Capability	What to ask
Model access	Can you choose among frontier and open models, switch or upgrade models without rewriting code, and get enough capacity at production scale?
Serving modes	Is there a tier for latency-sensitive traffic, and a dedicated-capacity option with performance guarantees?
Agent compute	Does it scale with traffic, keep long-running work alive through restarts and failures, and isolate each session?
Context	Does it provide durable conversation state, memory that persists across sessions, and secure access to business data and tools?
Quality	Can you trace each step and evaluate output with LLM judges, custom scorers and expert feedback?
Governance and cost control	Are there unified permissions, policies, guardrails, rate limits and usage tracking across models, agents and tools?
Framework freedom	Can you keep your framework, harness and model choices?

How does Databricks approach both layers?

Inference. Model Serving supports Foundation Model APIs for accessing foundation models from a serving endpoint, in these modes:

• Pay-per-token is the easiest way to start, with preconfigured endpoints.
• Priority pay-per-token is opt-in per request with the service_tier parameter set to "priority". Databricks admits the request ahead of standard best-effort pay-per-token traffic for more consistent latency under load.
• Provisioned throughput provides endpoints with optimized inference for foundation model workloads that require performance guarantees. Databricks recommends it for production workloads, in on-demand and reserved options.
• AI Functions run batch inference with any AI or ML model.

Agents. Agent Bricks is the Databricks agent developer platform. Databricks says it supports any agent harness, including open-source frameworks such as LangGraph, Agno and CrewAI. Agent Runtime hosts the agent on Databricks Apps. DurableAgentServer, the recommended agent server for new agents, adds persistent run state and crash recovery. MLflow Tracing records each step the agent takes, and Unity Catalog governs the data, tools, models and agents.
Governance and spend. Unity Gateway, built on Unity Catalog, enforces consumption limits on model and MCP services, distributes requests across model destinations with failover, and sets per-user thresholds and hard caps. You track requests, token usage and latency with system tables, and attribute cost to services, target models, principals and tags.
Combining harnesses. Databricks also offers a managed version of the open-source meta-harness Omnigent. It is in Beta on Databricks, and the open source README says "Status: alpha."

FAQs

What is the difference between inference optimization and an agent harness?

Inference optimization concerns how a model is served. The harness is the code around the model that reasons, calls tools and decides when to respond. A production agent needs both, plus state, runtime, observability and governance.

Can I use open-source frameworks for the harness?

Yes. Databricks says it supports any agent harness, including open-source frameworks such as LangGraph, Agno and CrewAI, and Agent Runtime hosts agents built with any framework or harness.

Which serving mode suits latency-sensitive traffic?

The docs describe priority pay-per-token as a capability for latency-sensitive, real-time applications. For production workloads that need performance guarantees, they recommend provisioned throughput.

How do I track spend across models and agents?

Use Unity Gateway to set per-user thresholds and hard caps, track token usage and latency with system tables, and attribute cost to services, target models, principals and tags.