Skip to main content
Version: 2.0

The application layer

Vectara is a headless platform. Between the end user and the Vectara agent runtime sits the application layer — the code that owns the UI, holds the user's identity, calls Vectara over REST, and streams responses back. This layer stays small because the platform does the AI heavy lifting underneath: retrieval, generation, factual grading, agent orchestration, governance, observability.

You always own this layer — the application code, the agent configuration, the data. There are two ways to get it built and running, both designed to be fast. Pick the one that matches how much of the work your team wants to take on.

OptionWho builds and operates itBest for
Build it yourselfYour team, using the API + Vectara Skills + a coding agentTeams that want control over the UI and the integration shape.
Vectara Managed AgentsVectara, on your behalfTeams that want a working app delivered turnkey, drawing on patterns Vectara has refined across enterprise deployments.

Both options sit on the same Vectara Enterprise Platform. The platform — RAG, agent runtime, models, security, observability — is identical underneath. In both cases the application, its configuration, and its data remain yours; the only difference is who does the building and operating.

Option 1 — Build it yourself​

The platform is built to be easy to ship on. A working chatbot, a search widget, or a stepped business workflow is a thin app around the Vectara API: stream events back to a UI, pass the user's identity in session.metadata, render the citations. The platform handles everything that actually involves AI.

The accelerators that make this fast:

  • Vectara Skills — packaged patterns Claude Code, Cursor, and other coding agents read directly. The coding agent already knows the right shape for tool_configurations, $ref, argument_override, next_steps, the streaming event protocol — no stale-training-data drift, no "is this field real?" round trips.
  • Reference implementations — connector wrapper apps, debug dashboards, embeddable chat widgets, stepped agents from a whiteboard sketch. Each one is a ready-to-paste prompt for your coding agent. See Build with coding agents for the 30-minute recipes.
  • The Agent Playground — drive any agent live before you commit to UI code. See Try the playground.
  • REST + typed events — no SDK lock-in, no proprietary protocol. Anything that speaks HTTP can host a Vectara-powered agent.

What this means in practice: a backend engineer with no AI background can ship a real agent + UI in 30 minutes, working with a coding agent. The platform is the AI engineer. Your coding agent is the application engineer.

This is the right path when you want to embed the agent deep inside an existing product, control the UI and roadmap, or use a workflow shape that isn't on the Managed Agents starter list.

Option 2 — Vectara Managed Agents​

Vectara Managed Agents is a service offering: Vectara delivers your agent application turnkey, drawing on the patterns, benchmarks, and operational know-how we have refined across enterprise deployments. No build phase on your side. You provide the use case, the data, and SME validation. Vectara delivers a working, branded, instrumented application — and keeps it tuned and current as the platform evolves.

The same outcome you would expect from an out-of-the-box product, with all the flexibility of the underlying platform when you need it. The application remains yours — your data, your tenant, your configuration — Vectara just does the work.

What Vectara delivers:

  • Branded agent application — UI customization, widget embedding, search and chat surfaces tailored to your brand.
  • Agent configuration — instructions, skills, custom tools, and workflows tuned to your use case, drawing on patterns proven across enterprise deployments.
  • Evaluation and metrics — eval suites and benchmarks specific to your data and your users; business-relevant metrics calculated on an ongoing basis.
  • Admin UI for business owners — non-engineers tune behavior, prompts, and routing without filing a ticket and without engaging your engineering team.
  • Accuracy, latency, and stability — Vectara takes responsibility for the SLOs end-to-end.
  • Continuous improvement — agents stay current as new agentic features and platform improvements ship. Tuning happens continuously, not on your sprint cadence.

What your team provides:

  • Requirements and use-case definition.
  • Access to data sources and the metadata schema.
  • SME validation of agent outputs.
  • User entitlement and group mapping (typically via your IdP).

Proven starting points: enterprise knowledge search and chat, customer-facing support chat, marketing search and discovery, internal employee productivity assistants, technical R&D assistants.

The application is visible and configurable by your team at all times — Managed Agents is not a black box. The underlying agent JSON, corpora, and configuration are inspectable in your tenant, and you can take operations back in-house at any point without re-platforming.

Hybrid is fine​

Most real deployments end up mixed. Vectara Managed Agents may handle one workload (a public-facing search chat) while your team builds a second one (a deeply integrated internal workflow) on the same platform. Both sit on the same tenant. Both see the same governance, the same observability, the same security boundary.

What stays the same in both options​

Everything below the application layer is identical:

LayerProvided by Vectara in both options
Agent runtime — steps, sub-agents, tool dispatch, structured outputs✅
Retrieval engine — Boomerang embeddings, hybrid search, Slingshot reranking, citations✅
Hallucination grading — HHEM scoring, Hallucination Corrector✅
LLM gateway — Anthropic, OpenAI, Gemini, on-prem, BYO✅
Pipelines, connectors, dead-letter queue✅
Tenant isolation, RBAC by corpus, audit trails✅
SOC 2 Type II, HIPAA, KMS-managed encryption✅

The choice between building yourself and Managed Agents is only about who does the work above the API. You own that layer either way. The platform is the same.

How to decide​

Choose Build it yourself when...Choose Managed Agents when...
You want to embed the agent deep inside an existing product you operate.You want a working app delivered turnkey, drawing on patterns Vectara has refined across enterprise deployments.
Your team wants control of the UI and the roadmap.You don't have application engineers to spare, or want to skip the build phase entirely.
You want to iterate on the UI weekly.You want business owners to tune the app from an Admin UI without involving your engineering team.
You're comfortable wiring up the REST API. (With coding agents and Vectara Skills, this is hours, not weeks.)You want Vectara to take responsibility for accuracy, latency, and stability SLOs end-to-end.

If you're unsure, start with the playground — drive an agent live with your own API key and a small corpus, and decide from there. Many teams start by building themselves to learn the surface, then move a production workload to Managed Agents once the use case stabilizes.

Last updated on by Nikhil Bysani