Knowledge Retrieval Agent
Finds the precedents and materials for each request
Tool: Document retrieval · Source: Engagement documents
Industry
Business and professional services AI agent operations
Engagement documents, knowledge bases, delivery plans and client reporting
Operational layers in view
Client documentsKnowledge baseEngagement planQuality reviewClient reporting
Knowledge Retrieval Agent
Drafting Agent
Quality Review Agent
Engagement Planning Agent
Risk and Contract Agent
Client Reporting Agent
Node activity below is a demonstration using sample business and professional services data.
Ask the live agents
Describe the problem in your own words. We classify the industry, assign specialist agents, and you watch the workflow run and produce the visual deliverables — maps, dashboards, charts, tables and reports — instead of one block of text.
Demo mode uses clearly labelled sample data — never presented as verified.
Assigned specialists
Business and professional services
- Knowledge Retrieval Agent
- Drafting Agent
- Quality Review Agent
- Engagement Planning Agent
- Risk and Contract Agent
- Client Reporting Agent
Deliverables planned
Dashboard
KPI panel
Charts
Data tables
Recommendations
Pauses for human approval
Partner approval
Workflow the run follows
- 1. User request
- 2. Requirement validation
- 3. Industry classification
- 4. Specialist agent selection
- 5. Data and file collection
- 6. Tool authorisation
- 7. Execution plan
- 8. Multi-agent collaboration
- 9. Analysis or generation
- 10. Quality validation
- 11. Human approval
- 12. Visual deliverables
- 13. Monitoring and audit
Demo
Business and professional services AI agent operations centre
Workflow: Client request received → Monitor engagement · Run pending · started — · elapsed 0s
Active agents
0
Tasks running
0
Completed steps
0/9
Tool calls
0
Human approvals
1
Warnings
0
Success rate
0%
Est. completion
36s
Active agents
Pending
Drafting Agent
Produces first-pass deliverables grounded in sources
Tool: Document retrieval · Source: Engagement documents
Quality Review Agent
Checks accuracy, citations and completeness
Tool: Document retrieval · Source: Engagement documents
Engagement Planning Agent
Builds delivery plans and resource needs
Tool: Document retrieval · Source: Engagement documents
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch business and professional services agents work through the workflow with sample data.
Demonstration using sample data — no real client operation is shown. Sign in and connect an approved AI provider and authorised industry data source to activate live execution.
Business and professional services specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Knowledge Retrieval Agent
Finds the precedents and materials for each request
- Inputs:
- Engagement documents, Internal knowledge base, Time and delivery records
- Tools:
- Document retrieval, Drafting model, Quality checker
Autonomous
Drafting Agent
Produces first-pass deliverables grounded in sources
- Inputs:
- Engagement documents, Internal knowledge base, Time and delivery records
- Tools:
- Document retrieval, Drafting model, Quality checker
Approval required
Quality Review Agent
Checks accuracy, citations and completeness
- Inputs:
- Engagement documents, Internal knowledge base, Time and delivery records
- Tools:
- Document retrieval, Drafting model, Quality checker
Autonomous
Engagement Planning Agent
Builds delivery plans and resource needs
- Inputs:
- Engagement documents, Internal knowledge base, Time and delivery records
- Tools:
- Document retrieval, Drafting model, Quality checker
Approval required
Risk and Contract Agent
Flags contractual and professional-risk issues
- Inputs:
- Engagement documents, Internal knowledge base, Time and delivery records
- Tools:
- Document retrieval, Drafting model, Quality checker
Autonomous
Client Reporting Agent
Prepares the client-facing status pack
- Inputs:
- Engagement documents, Internal knowledge base, Time and delivery records
- Tools:
- Document retrieval, Drafting model, Quality checker
Solution blueprint
Operational context
Industry challenges
- Operational data for business and professional services is spread across disconnected systems
- Analysis-heavy work is repeated manually every cycle
- Risks and exceptions are found late
- Reporting and audit overhead grows with volume
Solution patterns
Recommended AI solutions
- Specialist agents that own one step of the workflow each
- Grounded retrieval over your permitted systems only
- Predictive scoring with explainable reasons
- Approval-gated execution into your systems of record
Data readiness
Required data
- Engagement documents
- Internal knowledge base
- Time and delivery records
- Contract terms
Controlled process
Example workflow
- Client request received
- Validate scope and permissions
- Retrieve precedents
- Draft deliverable
- Quality review
Connection points
Integration opportunities
- Document management
- Practice management
- CRM
- Billing
Measure after baseline
Expected outcomes
- Analysis cycle time
- Manual review effort
- Decision auditability
- Earlier risk detection
Governance controls
Security and compliance
- Row-level security scopes every read to the requesting organisation
- Retrieval is limited to documents the user may already open
- Every agent action, tool call and approval is written to the audit log
- Sensitive steps pause until a named human approves them
Inputs, integrations and outputs
Inputs
- • Engagement documents
- • Internal knowledge base
- • Time and delivery records
- • Contract terms
Integrations
- • Document management
- • Practice management
- • CRM
- • Billing
Outputs
- • Draft deliverable (PDF)
- • Quality review log (CSV, PDF)
- • Engagement dashboard (Dashboard)
Business outcomes to baseline and measure
Analysis cycle time
Target and measurement method agreed during discovery.
Manual review effort
Target and measurement method agreed during discovery.
Decision auditability
Target and measurement method agreed during discovery.
Earlier risk detection
Target and measurement method agreed during discovery.
No performance result is promised before a baseline is measured. Targets are agreed for each engagement and reported against real delivery data.
How This AI Agent Works
The process the business and professional services agent team follows, from trigger to approved deliverable.
Schedule and risk workflow
From a project plan to approved task updates and progress monitoring.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. Project plan: Input. Schedule, WBS or task export uploaded to the workspace.
- 2. Review scope: AI agent. Check deliverables, owners and missing dependencies.
- 3. Analyse schedule: Approved tool. Compute critical path, float and slippage.
- 4. Detect dependencies and risks: AI agent. Rank risks with likelihood, impact and trigger.
- 5. Recommend actions: AI agent. Propose resequencing and mitigation options.
- 6. Project manager approval: Human approval. The PM approves before any task is changed. Human approval required.
- 7. Update approved tasks: Action. Write only the approved changes back.
- 8. Status and risk report: Output. Updated schedule view, risk register and actions.
- 9. Monitor progress: Monitoring. Track the approved actions to closure.
Approval gate: Project manager approval. Deliverable: Report.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| project-management-agent | 9 | Yes. Policy: always | Report | Available | Available (signed in) | Available via Studio · requires ai_provider | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Relevant case study
Illustrative case study
Wastewater network GIS for a national water utility
A wastewater network needed utility-GIS structuring, mapping and quality-controlled network intelligence for daily operations.
View all case studiesReady to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.