Features
Everything an agent needs, explained
Eleven capabilities, grouped by what they're actually for — not a features list, a working explanation of each one and who reaches for it.
Build & run
Design agents once, run them reliably
Orchestration
An Agent in APEXVYRA is a configured AI worker: a goal, a set of tools it's allowed to call, and the models it can use to get there. Orchestration is how you connect agents into a Workflow — an ordered sequence of steps that can branch on conditions, call multiple agents in sequence, and hand off partial results between them. You describe the shape of the process once; APEXVYRA handles retries, timeouts, and step ordering underneath it.
Who it's for: teams moving past a single prompt-and-response integration into multi-step processes — a refund that needs a lookup, a policy check, and an approval step, for example.
Agent Execution
Every time a Workflow runs, that's a Run — its own record with a status, a duration, and a cost. Agent Execution is the runtime that carries out each step: calling the routed model, invoking any tools the agent is allowed to use, and passing structured results to the next step. Runs are isolated from each other, so a failure in one doesn't affect another executing at the same time.
Who it's for: engineering teams who need execution behavior — retries, timeouts, concurrency limits — to be a platform guarantee, not something re-implemented per workflow.
Run #8841
Succeeded| Step | Duration | Status |
|---|---|---|
| lookup_order | 120ms | OK |
| issue_refund | 308ms | OK |
AI Workflow Automation
Not every Workflow needs a human to start it. An Automation Run is a Workflow triggered on a schedule or by an event — a webhook, a new row in a connected data source, a message in a queue — rather than a manual API call. The workflow definition doesn't change; only how it starts does.
Who it's for: teams running agents as background processes — nightly reconciliation, inbound-lead qualification, ticket triage — where nobody should need to be in the loop just to kick things off.
Route & version
Send every request to the right model — on purpose
Model Routing
A Routing Policy is the rule set that decides which model handles a given request: by cost, latency, capability, or a fallback order you define. Instead of hardcoding a model name in your application, you point at a policy — "balanced," for example — and APEXVYRA resolves it to an actual model at request time, including automatic fallback if the primary model is degraded or rate-limited.
Who it's for: teams who want to change which model handles production traffic — to cut cost, adopt a new model, or route around an outage — without a code deploy.
Prompt Versioning
A Prompt Version is an immutable, diffable snapshot of a prompt template. Every edit creates a new version rather than overwriting the last one, so you can compare exactly what changed, roll back a regression, and see which version was active for any historical Run.
Who it's for: teams that have been burned by a prompt edit that quietly changed production behavior with no record of what it used to say.
Ground & retrieve
Give agents your own data, not just their training data
Knowledge Base Sync
A Knowledge Base is a managed collection of your documents, synced on a schedule or via webhook from wherever they already live — so it stays current without a manual re-upload every time a source document changes. Suits teams grounding agents in documentation, policy, or a support corpus that changes regularly.
Vector Embeddings Cache
Turning a document into a retrievable vector costs a model call. The Embedding Cache stores and reuses those vectors instead of recomputing them on every retrieval, so re-indexing an unchanged document costs nothing. Matters most for large or frequently-queried knowledge bases.
Retrieval-Augmented Generation
RAG is how an agent answers using your data instead of only what a model learned during training: a query is embedded, matched against the Embedding Cache, and the most relevant chunks are added to the model's context before it generates a response. For any agent that needs to be right about specifics, not just plausible in general.
Context Management
The Context Manager assembles what a model actually sees for a given step — conversation history, retrieved chunks, and tool outputs — trimmed and ordered to fit the model's context window without truncating the parts that matter. For agents with long conversations or large retrieved documents.
Verify & observe
Know what your agents did, and catch it before it ships
Evaluations & Guardrails
An Evaluation is a scored test of an agent or Prompt Version against a set of defined cases — run before a change ships, not discovered after. A Guardrail is a constraint enforced automatically on every Run: a content check, a cost ceiling, a restriction on which tools can be called. Guardrails run whether or not anyone remembers to check.
Who it's for: teams who need a change to pass a test before reaching production, and a floor under what a live agent is allowed to do.
Observability
Every Run produces logs, traces, and cost data — not just that it happened, but the full sequence of steps, which model handled each one, and what it cost, searchable in one place.
Who it's for: whoever gets paged when an agent does something wrong and needs to find out why in minutes, not by reconstructing it from application logs.
| Run | Model | Cost | Status |
|---|---|---|---|
| #8841 | Balanced | $0.0021 | Success |
| #8840 | Fast | $0.0004 | Success |
| #8839 | Balanced | $0.0038 | Failed |