Service Operations
PatternTicket triage and resolution
Classify, enrich and route inbound requests; resolve the well-understood cases; escalate the rest with context attached.
Agents that do the work, inside real boundaries.
An agent is only useful if it can act on the systems that matter, and only safe if what it can do is explicitly bounded. We design agents as operational components: narrow scope, declared tools, recorded actions, and a human path for anything outside their remit.
The business problem
Autonomy without boundaries is not automation.
General-purpose agents demo well and deploy badly. In an operational setting the questions are not whether the agent can complete the task but what it is permitted to touch, what happens when it is wrong, and who can reconstruct the decision afterwards. Those are design constraints, not guardrails added later.
What that looks like
- No explicit permission model, so scope creeps with every prompt change
- Actions taken against live systems with no reconstructable trail
- Nothing distinguishes low-confidence output from high-confidence output
- No defined path for the cases the agent should not be deciding at all
What Catalyze builds
What we build
Agents designed the way you would design any other component with write access to a system of record.
- 01
Scoped agent design
One agent, one defined job. Narrow remit is what makes behaviour predictable and failure diagnosable.
- 02
Explicit tool boundaries
A declared set of actions against declared systems. Anything outside the set is not a prompt away — it does not exist.
- 03
Permission and identity model
Agents operate under their own identity with role-based access, so their reach is auditable and revocable.
- 04
Confidence and escalation
Output carries a confidence signal, and low-confidence or high-impact cases route to a human rather than proceeding.
- 05
Action audit trail
Every action, input and decision recorded in a form a reviewer or regulator can reconstruct after the fact.
- 06
Multi-step orchestration
Where a process spans several agents and systems, orchestration owns sequencing, retries and compensation.
Architecture
How an agent is bounded
The layers between an instruction and a write to a system of record.
01Trigger
An event, a queue item, a schedule or a human request enters the workflow.
02Context Assembly
The agent is given the records and definitions it needs from the semantic layer — and nothing else.
03Policy + Permissions
Identity, role and data-scope checks applied before any tool is available.
04Agent Execution
Reasoning and tool calls within a declared action set, each call logged.
05Verification
Deterministic checks and confidence thresholds applied to the proposed action.
06Human Review
Low-confidence, high-impact and policy-flagged cases route to a named owner.
07Commit + Audit
The action is written to the system of record with a complete, reconstructable trail.
Example use cases
Where this applies
Patterns we design for. Items marked as published link to our own write-up.
Back Office
PatternMulti-system process completion
Complete a process that spans several systems end to end, with compensation steps when one of them fails mid-sequence.
Technology & integration
Integration surface
Agents act through the same integration surface as the rest of the system — no side channels.
Named platforms
- Salesforce
- Workday
- ServiceNow
- AWS
Action surface
- System-of-record APIs
- Workflow engines
- Message queues
- Document stores
Controls
- Agent identity
- Role-based access
- Action allow-lists
- Immutable audit log
Reliability
- Retries + backoff
- Idempotency
- Compensating actions
- Dead-letter review
Case studies
Proof in production.
Related insights
Our thinking on this.
Questions
What buyers ask us about Agentic Workflows.
How much autonomy do these agents actually have?
As much as the declared action set allows and no more. Autonomy is a property of the boundary, not of the model — we set the boundary per workflow based on the cost of being wrong.
What happens when an agent gets something wrong?
Verification runs before commit, so most errors are caught as low confidence and routed to a human. Where something does commit incorrectly, the audit trail supports reconstruction and the orchestration layer supports compensating actions.
Do agents replace the team doing this work today?
The pattern we design for is people supervising exceptions rather than processing volume. That changes what the role is; whether it changes headcount is an operating decision, not a technical one, and one we would rather discuss honestly up front.
Can agents work across several systems in one process?
Yes — that is usually the point. When a process spans systems, orchestration owns sequencing, retries and rollback so that a partial failure does not leave the operation in an inconsistent state.
Tell us about the workflow. We'll tell you whether Agentic Workflows is the right place to start.