Custom AI workflow agents, built around your business
Turn incoming requests, documents, and business information into defined next steps. Service Time Lab builds custom AI workflow agents that use approved tools and involve your team when judgment or approval is needed.
Workflows an agent can help with
These examples would be tailored to your business systems, rules, and approval requirements.
Lead response
Collect missing details, check service criteria, create a customer record, and draft a response for review.
Customer support
Use approved account and policy information to draft answers, update tickets, and route exceptions.
Document processing
Extract and validate submitted information, update approved records, and flag missing or inconsistent details.
Internal knowledge
Answer employee questions from approved documents and show the sources used.
Scheduling
Check approved availability, propose options, and record the confirmed selection.
Proposal preparation
Apply approved business rules to prepare a draft and request approval before sending.
Stage 01Workflow Agent Assessment
Find the right starting point
We map the task, systems, people, and bottlenecks to decide whether an AI workflow agent is useful. The assessment gives you a proposed scope and a basis for deciding what to build.
Typical deliverables
- Workflow map and agent-fit recommendation
- Agent responsibilities, systems, and integrations
- Approval points, data sensitivity, and risks
- Pilot success measures
- Proposed development scope and estimate
Stage 02Custom agent pilot
Test one workflow before expanding
Build a working agent for one agreed task, then test it with the people who will use or supervise it. The results guide the decision to refine, deploy, or stop.
Typical deliverables
- Working agent with initial integrations
- Permissions, approval steps, and exception handling
- Realistic test cases and evaluation results
- User feedback and production-readiness recommendation
Stage 03Integration and deployment
Connect the systems your team uses
Connect email, customer records, calendars, documents, or internal applications where suitable interfaces and approved access are available.
Feasibility depends on your systems, available interfaces, data quality, and security requirements.
Work may include
- Authentication and controlled credential handling
- System connections and saved workflow progress
- Logs, retries, and failure handling
- Deployment and human-review interfaces
- Documentation, training, and support planning
Stage 04Evaluation and improvement
Improve with evidence from real use
Define success criteria, test expected and failure cases, and review agent activity and user feedback. Use the findings to guide improvements in quality, response time, and operating cost.
Work may include
- Test datasets and quality checks
- Activity-log and failure review
- Prompt, tool, and model-change testing
- Cost, response-time, and expansion review
One engineering team from discovery to deployment
Forward Deployed Engineering is how we deliver custom AI workflow agents. The engineers who map the workflow also build, integrate, test, and refine the application with your team.
Collaboration may be remote, onsite, or hybrid within the agreed engagement.
See How We Build AgentsAgreed before work begins
- Workflow, systems, environments, and approved access
- Client participants and responsibilities
- Measurable acceptance criteria and delivery plan
- Documentation, handoff, and support terms
- Approval process for additional scope
The application behind your agent
We build the interfaces, integrations, and data services your workflow needs. Our approach is technology-agnostic: we choose frameworks and models to fit your systems, security requirements, and long-term support needs.
Our primary agent-building technologies include:
Microsoft Agent Framework
Build AI workflow agents that connect models and approved tools, with defined workflow steps and human review.
LangChain ecosystem
Use LangChain for agent development, LangGraph for workflow control, and LangSmith for tracing and evaluation.
Questions before you start?
Find more on project scope, pricing, data handling, and support in our full FAQ.
What does forward-deployed engineering mean?
The engineers who understand your workflow stay involved in building and introducing the solution, working directly with your team.
Is an agent just a chatbot?
An AI workflow agent can use approved tools to update records, prepare work, and request approval as well as answer questions.
What if ordinary automation is a better fit?
We will recommend conventional automation, a software feature, or a process change when an agent would add unnecessary cost or risk.
Start with one workflow worth improving
Tell us:
- The task your team repeats
- The systems involved
- The result you want to improve
We will help assess whether an AI workflow agent fits the work and what a useful starting scope could be.
A plain-language description is enough to start. Please leave out confidential or sensitive data.