Product family
Product conceptAI Agent Workspace
Agents that plan, act and hand back to people at the right moment.
A workspace for running task-specific AI agents against your own tools and data, with clear permissions, approval steps and a full activity trail.
Product concept. This is not a self-serve product yet. There is no public account, live dashboard or price list; we deliver it as a configured build and will show you the architecture, not a staged demo.
/ 01Overview
What it is and why it matters
Most teams do not need a general-purpose autonomous agent. They need a few dependable agents that each do one job well: triage a queue, prepare a renewal pack, reconcile a report, chase a missing document. The AI Agent Workspace is our product concept for running that kind of agent in one governed place.
Each agent is defined by a goal, the tools it may call, the data it may read and the points where a person must approve. Agents work through a plan, call tools through scoped connectors, and post their work for review instead of acting silently. Every step is logged so a reviewer can see what the agent saw, what it decided and why.
Today this is a product concept that we deliver as a configured build on top of our engineering practice. If it matches a problem you have, we will scope it with you, show the architecture and agree what a first version should and should not do.
/ 02Features
How it works
01
Agent definitions as configuration
Goal, instructions, allowed tools, data scopes and escalation rules live in versioned configuration, so changes are reviewed like code.
02
Scoped tool connectors
Agents call your CRM, ticketing, storage or internal APIs through connectors with least-privilege credentials and per-action limits.
03
Approval checkpoints
Any action that writes, sends or spends can require a named approver. Reviewers see the proposed action, the evidence and a diff before confirming.
04
Run history and replay
Every run records prompts, tool calls, retrieved context and outputs. Runs can be replayed against new model versions before you switch.
05
Evaluation harness
Each agent ships with test scenarios and pass criteria, run before every change, so quality is measured instead of assumed.
06
Cost and rate controls
Budgets per agent and per run, with model routing so routine steps use smaller models and hard steps use stronger ones.
/ 03Actual capabilities vs roadmap
What a first release includes, and what it does not
In scope for a first build
- Single-purpose agents with tool use, planning and structured outputs
- Human approval steps for write, send and spend actions
- Role-based access to agents, data sources and run history
- Run logs, replay and scenario-based evaluation
- Deployment in your cloud account or a dedicated environment
Roadmap, not yet delivered
- Visual builder for non-technical agent owners
- Multi-agent hand-offs with shared memory policies
- Marketplace of pre-built connectors for common SaaS tools
/ 04Use cases
Where it earns its keep
Service desk triage
An agent reads incoming tickets, gathers account context, proposes a category, priority and first response, and routes edge cases to a person.
Sales operations preparation
Before a renewal call an agent assembles usage, open issues and contract dates into a brief, then drafts follow-ups for the account owner to approve.
Finance exception handling
An agent investigates unmatched transactions, pulls supporting documents and proposes a resolution that a controller accepts or rejects.
Related solutions: Customer Support & CX, Sales & Lead Operations, IT & Engineering
/ 05Integrations
Connects to the systems you already run
Listed systems are integration targets reached through their documented APIs. Their mention does not imply a partnership or endorsement.
Work systems
- Salesforce, HubSpot or Dynamics
- Zendesk, Freshdesk or ServiceNow
- Jira and Confluence
- Slack or Microsoft Teams
Data and storage
- SharePoint, Google Drive, S3
- PostgreSQL, Snowflake, BigQuery
- Internal REST and GraphQL APIs
Model providers
- Commercial model APIs under enterprise terms
- Open-weight models in your own environment
/ 06Security and governance
Designed for oversight
- Agents never hold broader credentials than the task needs; secrets stay server-side in your vault.
- Write actions are gated by policy and, where required, by named human approval.
- Prompts, tool calls and outputs are logged with retention you control; sensitive fields can be redacted before logging.
- Prompt-injection defences: untrusted content is isolated from instructions and tool calls are validated against schemas.
/ 07Demo
No live product demo yet
There is no live demo of AI Agent Workspace because it is not a released product. Our demo lab includes a browser simulation on synthetic data that shows one of the core patterns behind it: “Agent proposal and human approval”.
/ 08Pricing or quote
Priced to your scope, never invented
There is no self-serve price list. A first version is scoped as a fixed-scope build or a pilot, with running costs estimated from expected volume and model choice.
/ 09FAQ
Questions about AI Agent Workspace
Is the AI Agent Workspace available to sign up for today?
No. It is a product concept that we deliver as a configured build for a specific workflow. We are clear about this so that you can judge it on the engineering, not on a demo account.
How do you stop an agent from doing something it should not?
By limiting what it can reach, validating every tool call against a schema, requiring approval for consequential actions and testing it against scenarios that include adversarial inputs before release.
Which models does it use?
It is model-agnostic. We choose per step based on quality, latency, cost and data-residency needs, and we re-run the evaluation suite whenever a model changes.
Can it run inside our own cloud?
Yes. Deployment into your cloud account or a dedicated tenant is the default for anything that touches sensitive data.
Related services
- Service / buildAgentic AI & Multi-Agent SystemsAgents that use tools to complete multi-step work, engineered with limits, approvals and evaluation.
- Service / operateEvaluation, Guardrails & ObservabilityKnow how your AI systems behave: test suites, runtime guardrails, tracing and quality monitoring.
- Service / integrateAI Workflow & CRM/ERP IntegrationBring AI into the CRM, ERP and service platforms where work already happens.
Explore AI Agent Workspace with our team
A 45-minute conversation with an engineer: your workflow, the architecture, the risks and what a first version would include.