AI That Acts, Backed By Your Data
Conversational AI was the first step. Agentic AI is where it starts doing real work.
AI becomes more valuable when it can take action, not just answer questions. But once AI starts updating records, routing work, or triggering business processes, governance becomes just as important as automation.
TeamSync's Agentic AI Workflow lets organisations build AI agents that operate within clearly defined business rules. Every action, whether it succeeds or is refused, is logged to the audit chain. EU AI Act Article 11/13/14 documentation is generated directly from that chain.
Talk to the AI solutions team · Read the agentic workflow pillar · See the Chief AI Officer page
What's In The Agentic Surface
Sub-capability | What it does |
Agent designer | Define what the agent is for, what it's allowed to do, and where a human needs to review its work |
Tool surface enforcement | Business rules define which actions are allowed; the platform enforces this |
Bounded retrieval | The agent only retrieves what the person it's acting on behalf of is allowed to see |
Action logging | Every action, attempted, successful, or refused, is logged to the audit chain |
Human-in-the-loop checkpoints | Configurable review gates at whatever risk threshold you set |
Multi-agent orchestration | Agents can hand tasks off to other agents, under explicit policy |
EU AI Act documentation generation | Articles 11/13/14 documentation is generated from the audit chain |
Performance monitoring | Tracks agent effectiveness, refusal rate, and escalation rate |
What "Bounded Autonomy" Actually Requires
Stage | Standard agentic frameworks | TeamSync |
Tool surface | Defined by prompts, followed on a best-effort basis | Defined in business rules, enforced by the platform |
Permissions | Often the agent's own permissions, not the user's | Always the on-behalf-of user's permissions, for every action |
Audit per action | Optional, and varies by framework | Built in, every action is logged |
Human-in-the-loop gates | Enforced at the application layer | Enforced at the platform layer, and can't be bypassed |
EU AI Act documentation | Built manually | Generated automatically from the audit chain |
Multi-agent handoff | Managed by the application | Managed by the platform, under policy |
For a production agentic deployment, a CISO's review depends on each of these being built into the architecture, not just handled procedurally.
What An Agent Looks Like In Practice
Take a claims-triage agent as an example:
Element | Configuration |
Purpose | Sort incoming claims into the right routing categories |
Tool surface | Read claim documents in the user's queue, classify them against the taxonomy, route to the right handler, escalate to a human above a set claim value |
Bounded retrieval | Only reads documents the on-behalf-of user can see |
Human-in-the-loop | Claims above the set value need human approval before routing |
Audit anchor | Every retrieval, classification, routing decision, and escalation is logged |
Refusal logging | If the agent tries an action outside its allowed scope, that refusal is logged with the reason |
Six months later, when an auditor asks "what did the agent do, and was every action authorised?", the answer comes from a single query against the chain.
Where Customers Deploy Agentic AI
The most common patterns we see:
Workflow | What the agent does | Where the human stays involved |
Claims triage | Reads, classifies, routes, summarises | Approves anything above a set value; handles complex claims |
Contract first-pass review | Flags risk clauses, summarises against the playbook | Reviews risk clauses; handles final negotiation |
Trade surveillance | Flags potentially anomalous communications | Final review and decision |
Quality event triage | Classifies non-conformance, routes to the CAPA owner | Handles investigation, root cause, and corrective action |
FOIA response | Identifies responsive documents, drafts redactions | Approves final redactions and sign-off |
TMF completeness check | Identifies missing documents, drafts notifications | Uploads documents, gives final approval |
In each case, the agent handles the slowest parts of the process, freeing the human to focus on judgment calls.
How TeamSync Compares
Customers typically evaluate this against:
Microsoft Copilot Studio + Power Automate: Strong for M365-based workflows, but weaker on cross-source coverage, per-action permission checks, and EU AI Act documentation
Salesforce Agentforce: Strong within Salesforce, but weaker as a regulated-content platform
In-house frameworks (LangChain, LlamaIndex, AutoGen, CrewAI): Most flexible, but bounded autonomy, audit logging, and regulatory documentation all need to be built yourself
n8n / Zapier with AI nodes: Strong visual workflow design, but weaker on regulated-content audit
For specific comparisons:
- TeamSync vs M365 Copilot
Read Further
Why TeamSync — agentic AI workflow — the architectural pillar
Chief AI Officer page — the AI program view
Business Rules capability — the rules engine that defines tool surfaces
Business Process Automation — the workflow engine the agent orchestrates
EU AI Act overlay — the regulator-specific documentation pack