The situation
A support process gathers context from several internal systems before a person approves an account change.
Agentic AI Workflows
An agentic workflow uses a model to help coordinate a sequence of tasks and tools. Production design must define what the workflow can access, which actions it may take and when a person must decide.
- Bound the task
- Connect tools deliberately
- Evaluate before release
Is this right for you?
When this is the right work.
A repeated business workflow spans knowledge, APIs and decisions, and a single chat response cannot complete it usefully.
The first conversation establishes the environment, the constraint and who will accept the result. We agree a bounded starting point before expanding the engagement.
Scope
What DotConn delivers.
- 01
Bound the task
Define the workflow, success conditions, permitted data and actions that require human approval.
- 02
Connect tools deliberately
Implement tool access, state, failure handling and a record of consequential actions.
- 03
Evaluate before release
Test normal tasks, missing information, denied actions and recovery from partial failure.
Deliverables
What you receive.
Acceptance criteria identify the checks, records and handover needed for the agreed scope.
- Workflow and decision-boundary specification
- Implemented tool orchestration
- Evaluation cases and operating guidance
In practice
A practical example.
Our approach
We automate the permitted information gathering and keep the account decision behind an explicit approval boundary.
The useful result
The customer can inspect actions, exceptions and the evidence supporting a decision.
Responsibility
Agree the responsibility.
DotConn
Assesses and delivers the technical work in the agreed scope, with reviewable changes and handover. Continuing operation requires a separately defined managed-service scope.
Your team
Provides authorized access, business priorities and accountable decision-makers; approves policy choices and accepts the agreed result.
Set this boundary early
An agent is not given unrestricted authority. Accuracy, cost and tool behavior require ongoing evaluation; no autonomous performance or productivity guarantee is implied.
FAQ
Questions worth resolving.
Technical reading
Does every workflow need an agent?
No. A deterministic integration may be more reliable for a fixed process. We evaluate where model-based reasoning adds useful capability.
Pricing, timing and how to prepare are covered in working with DotConn.
Connected work
Services that work alongside this.
Retrieval-Augmented Generation
RAG retrieves relevant material and supplies it as context to a language model. A useful system must evaluate both the retrieval step and the answer produced from it.
ExploreProduction LLMOps
LLMOps manages changes and operations for language-model applications. Prompts, model choices, retrieval configuration and tool permissions can all affect behavior and need a reviewable release path.
ExploreWorkplace AI Governance & IT Workflows
Workplace AI governance manages the organization’s use of internal AI tools: access, permitted information, procurement and human responsibility. It differs from engineering an AI feature into a customer-facing application.
ExploreVector Database Implementation
Vector retrieval searches representations of data for similarity. A vector-database implementation also needs the right metadata, filters, update behavior and a way to evaluate whether retrieved results are useful.
Explore