Agent creation with personas
Define who the agent is: role, tone, scope of authority and the persona it presents in conversation and in action.
Every agent: born, tested, certified and run in one studio
Agent Studio knows what an agent needs, because everything an agent needs is plugged into it. Prompts are drafted with AI, versioned, tried in a playground, tested and certified. Agents are composed from personas, tools, skills, knowledge and memory; released through a real release process; invoked by people, by other agents, over A2A, or through APIs for external consumption; orchestrated singly or in teams; and watched in production like any other workload. The studio exposes its agents over A2A, and consumes external agents the same way.
- 01Create Agent
- 02Prompt
- 03Playground
- 04Test & Certify
- 05Release
- 06Observe & Improve
Create the agent: persona, tools, skills
Every agent starts here: an identity and persona, the tools it may call, the skills it carries, and its duty, conversing with people or pursuing a purpose autonomously. The studio assembles all of it in one place.
Tools onboarding
Give agents hands: onboard the tools they may call, including custom tools written in the studio's programming language for anything your estate needs.
Skills creation & association
Build reusable skills once, associate them across agents, capability grows as a library, not per-agent copy-paste.
Conversational & autonomous agents
Create agents for either duty: conversational agents that work with people, and autonomous agents that pursue a specific purpose unattended.
The Prompt Studio: every agent’s brain, engineered
Behind every agent sits a prompt, and the studio treats prompts as source code with a dedicated workshop of their own: authored with AI assistance, versioned, exercised against real models in a playground, and certified by structured tests before any agent may carry them into production.
Prompt creation, AI-assisted
Draft agent prompts with AI helping you write, critique and tighten them, the studio suggests structure, edge cases and guardrails as you type.
Prompt versioning
Every prompt is versioned like code: diff any two versions, roll back, and know exactly which version each running agent carries.
LLM model selection & configuration
Choose the model behind each agent, cloud-hosted or GPU-backed, with full control of temperature, context, token budgets and provider-specific settings.
Prompt playground
Try prompts against live models before an agent exists, iterate on real responses, side by side across versions and models.
Prompt testing & certification
Structured test suites score prompts against expected behaviours; only certified prompts can ship into a production agent.
Ground it: knowledge, memory, integrations
Agents act on your reality, not model folklore: grounded in managed knowledge bases, equipped with short- and long-term memory that can be shared between agents, and connected to external systems through governed integrations.
Knowledge bases
Manage knowledge sources in the studio and attach them to agents, answers grounded in your documents, runbooks and data, not model memory.
Short- & long-term memory
Agents remember: session-scoped short-term memory and durable long-term memory, shareable between agents where the use case demands it.
External integrations
Define the external systems an agent may use, wired through the platform's governed connectors, scoped per agent.
Ship it: release, invoke, orchestrate
Production is where agent platforms usually stop, and where the studio keeps going: a release process with certification gates, agent-to-agent invocation in both directions, and a full orchestration layer beneath single agents and multi-agent teams.
Agent release process
Agents ship through a release pipeline: certification, approval, staged rollout and rollback, no prompt edit lands in production silently.
A2A invocations
Agents call agents, in both directions: the studio exposes its agents over A2A for external systems to invoke, and consumes external agents the same way, so specialists collaborate across platform boundaries.
Orchestration layer
A full orchestration layer underneath, single agents or multi-agent teams coordinated toward a goal, with the studio holding the plan.
One studio, the whole lifecycle. Nothing about an agent lives outside: the prompt that drives it, the tests that certified it, the tools and knowledge it uses, the memory it keeps, the release that shipped it and the telemetry it emits, all in the same place, all governed, all auditable.
AgentOps: every agent, watched and explained
Shipping an agent is the halfway point. AgentOps is the operations room for the fleet: how every agent performs on every task it runs, drill-down into each step it took, the models and providers behind it, and a triage feed that surfaces the agents drifting before users complain.
Operations at a glance
Invocations, success rate, latency P95, active users, token burn, total spend and cost per interaction, live across the fleet, split by conversational and autonomous agents.
Task & step drill-down
From fleet to agent to a single task to every step inside it: what was asked, what the agent reasoned, which tools and APIs it called, what came back, full explainability at every hop.
Fleet comparison
Agents ranked side by side on invocations, failure rate and cost, so the underperformer and the cost outlier are a glance, not an investigation.
Model & provider economics
Per-model traffic mix, tokens, cost and latency; provider reliability with error rates and weighted availability, know exactly what each model earns and costs you.
Triage feed
Anomalies surface themselves: latency deviation, token deviation, high failure rates and cost spikes, flagged per agent and model with severity attached.
Quality & safety signals
User feedback ratios and downvote reasons, security or privacy concern, too slow, confusing, tracked as first-class telemetry beside the technical metrics.
Responsible AI, measured, not promised. A governance scorecard scores every agent on explainability, transparency, fairness, accountability and security; departments are ranked; each agent is classified optimal, needs review, or critical, so a Responsible AI index is a number you track, not a slide you present.
Security & governance: the agent reasons, the governed layer acts
Agents in this studio operate inside an enforcement perimeter, not on trust. Prompts are screened before they reach a model, every call runs as the calling user and nothing more, data access is filtered attribute by attribute, and no agent ever touches data directly.
Prompt guardrails at the gate
Every prompt a user submits passes an LLM guard plus custom regex-based guardrails before anything else happens, prompt injection and malicious payloads are stopped at the front door, not discovered downstream.
RBAC on every call
When an agent calls an API or runs a query, the platform checks the calling user’s own token against that API. If the user does not have the access, the agent does not make the call, identity is never escalated through an agent.
ABAC through the AOA layer
Past the API check, the AOA layer applies attribute-based control: attribute filters and data filters shape what comes back, so a user sees only the data their attributes entitle them to, even when an agent fetches it.
No direct data access, ever
Agents never touch data. If a database query is needed, the agent generates it and the governance layer fires it; every read and write goes through designated APIs in the integration layer. The AI identifies which API or integration to call, the governed layer does the touching.
Bring an agent from idea to production
See the full lifecycle live, prompt to certification to release to A2A orchestration, on a use case from your own environment.