“Our operations involve much more than booking a moving job. Distil Stack approached the platform with a clear understanding of dispatch, drivers, foremen, customers, and payments, creating a connected system designed around the way our business actually operates.”
AI Agents & Workflow Automation
AI agent development services
We design AI agents and automated workflows that read, decide and act across your tools, with approvals, audit trails and clear limits on what they can touch.
A custom engineering engagement
We scope this service around your workflow, existing systems and acceptance criteria. The work starts with the problem and its constraints, then a written scope and price. A packaged solution is a different buying route: choose it when its defined deliverables already fit the job.
01The problem
Where things usually stall
Agents and automations that move work between your systems, with people in the loop.
- 01
Copy-paste between systems
Staff spend hours moving data from email to CRM to spreadsheet and back again.
- 02
Chatbots that stop at answers
Assistants can explain a policy but can't open the ticket, update the record or send the follow-up.
- 03
Automation nobody trusts
Brittle scripts break silently, so teams keep doing the work by hand just in case.
02Capabilities
What we deliver
- 01
Agent design
Clear roles, tools and permissions for each agent, defined around a specific job rather than a general assistant.
- 02
Tool and API integration
Agents connect to your CRM, ERP, ticketing, email and internal APIs through scoped, logged actions.
- 03
Human-in-the-loop approvals
Sensitive steps go to a person with full context, so automation speeds work up without removing oversight.
- 04
Retrieval over your knowledge
Agents ground their answers in your documents, policies and records, and cite their sources.
- 05
Evaluation and guardrails
Test suites, allowlists and output checks that catch bad actions before they reach production.
- 06
Monitoring and cost control
Dashboards for success rate, latency and spend per workflow, with alerts when something drifts.
03Outcomes
What changes once it’s live
- 01
Actions with clear limits
A defined set of permitted actions, connected systems and escalation rules for each agent.
- 02
Approvals where they matter
Sensitive changes wait for a person; routine actions follow the agreed workflow and access rules.
- 03
A workflow you can inspect
Evaluation results, execution records and cost visibility help your team understand what ran and why.
04Approach
How we deliver it
- 01
Bound the job
Map the trigger, systems, permissions and completion criteria for one workflow before adding autonomous decisions.
- 02
Build the evaluation set
Use representative tasks, failed tool calls and ambiguous requests to establish an acceptance baseline.
- 03
Connect and constrain
Implement scoped credentials, action allowlists, human approvals and safe handling of retries.
- 04
Release with an operator
Launch with monitoring, spend limits, runbooks and a named owner who can pause or investigate the agent.
05Stack
Tools we use most
- Claude
- LangChain
- Python
- n8n
- PostgreSQL
- Qdrant
- OpenAI
- LlamaIndex
- Pinecone
- Weaviate
- Ollama
- FastAPI
06Case studies
Featured success stories
What can an agent safely do on its own?
Whatever you approve. We start with read-only access and low-risk actions, then widen permissions as evaluation data shows the agent is reliable.
Will this replace our existing automation tools?
Usually not. Agents often sit on top of Zapier, Make or n8n flows and handle the steps that need judgment.
What happens when an agent gets something wrong?
Every action is logged and reversible where the underlying system allows it. Failures trigger alerts and fall back to a person.
How much does an AI agent cost?
$10,000 – $25,000 for a single-workflow agent; $25,000 – $60,000 for agents working across several systems. A single agent acting on one or two systems with human approval steps is the low end. Multiple agents across your CRM, ERP, and ticketing, with evaluation suites and cost controls, is the high end. Most of the cost is integration and hardening, not the model.
How should the first delivery decision be made?
Map the trigger, systems, permissions and completion criteria for one workflow before adding autonomous decisions. Use representative tasks, failed tool calls and ambiguous requests to establish an acceptance baseline.
What should the acceptance criteria cover?
A defined set of permitted actions, connected systems and escalation rules for each agent. Sensitive changes wait for a person; routine actions follow the agreed workflow and access rules. Evaluation results, execution records and cost visibility help your team understand what ran and why.
How do human approvals work?
We define which actions require a person before connecting the agent. The review shows the proposed action, its supporting information and its expected effect. Permissions and tool controls enforce the boundary; an instruction in the prompt alone is not an approval mechanism.
How long does an AI agent build take?
The approved delivery range is 8–12 weeks to production, with validation used to test a defined workflow before the full build. System access, approval requirements and integration boundaries determine the written scope. We agree dependencies rather than treating the range as a promise for every agent.






