AI Agents
An AI agent is software that can plan, decide, and take action across multiple steps of a task with minimal human intervention, connected directly to a business’s own tools and data. Trellist designs and builds custom AI agents that automate defined, repeatable work inside your existing systems, with governance built in from the start — because most agents that demo well die in production the moment they hit something outside their training.
What we do
Agent Strategy & Use-Case Discovery
We map your workflows against value, data readiness, and feasibility, and rule out the shiny-object use cases before any build work starts.
See the case studyCustom Agent Development
We build agents that plan, execute, and adapt across your actual business processes, using function calling, tool integration, and retrieval.
Tools we work with
Claude for reasoning, planning, and tool use
OpenAI for agents and function calling
Agentforce for agents inside Salesforce
Microsoft Copilot Studio for agents inside Microsoft 365
Enterprise System Integration
Every agent we build connects directly to the tools, APIs, and data sources your team already uses — no rip-and-replace.
Tools we work with
Model Context Protocol for secure connections to your tools and data
Human-in-the-Loop Governance
We design guardrails, approval checkpoints, and escalation paths so your team stays in control of decisions that require judgment.
See the case studyMulti-Agent Orchestration
For processes with multiple steps and handoffs, we coordinate specialized agents that each own a piece of the workflow.
See the case studyMonitoring & Continuous Improvement
We monitor performance, catch edge cases the agent wasn’t trained on, and refine behavior as your business changes.
Agent Strategy & Use-Case Discovery
We map your workflows against value, data readiness, and feasibility, and rule out the shiny-object use cases before any build work starts.
See the case studyCustom Agent Development
We build agents that plan, execute, and adapt across your actual business processes, using function calling, tool integration, and retrieval.
Tools we work with
Claude for reasoning, planning, and tool use
OpenAI for agents and function calling
Agentforce for agents inside Salesforce
Microsoft Copilot Studio for agents inside Microsoft 365
Enterprise System Integration
Every agent we build connects directly to the tools, APIs, and data sources your team already uses — no rip-and-replace.
Tools we work with
Model Context Protocol for secure connections to your tools and data
Human-in-the-Loop Governance
We design guardrails, approval checkpoints, and escalation paths so your team stays in control of decisions that require judgment.
See the case studyMulti-Agent Orchestration
For processes with multiple steps and handoffs, we coordinate specialized agents that each own a piece of the workflow.
See the case studyMonitoring & Continuous Improvement
We monitor performance, catch edge cases the agent wasn’t trained on, and refine behavior as your business changes.
Our approach
We start by mapping your workflows against value, data readiness, and feasibility, so the first build is the right one. From there we architect the decision logic, integration points, and guardrails before any production code gets written, then build against your real systems and data rather than a sandbox demo. We launch with monitoring in place and keep iterating as the agent meets real-world edge cases.
How it compares
An AI agent reasons across multiple steps, adapts to new information, and takes action across systems with minimal human input. That’s different from RPA, which follows fixed, pre-programmed rules and breaks the moment a process deviates from script — and different from a chatbot, which answers conversational questions but generally doesn’t take multi-step action across your business systems.
Why fractional, embedded teams win
- Firms running three or more AI use cases in production report roughly 4x the return of a single deployment (NMS Consulting, 2026).
- Partner-built AI succeeds at roughly twice the rate of AI built internally from scratch (MIT, 2025) — the surer bet is rarely going it alone.
FAQ
How long does it take to deploy a custom AI agent?
It depends on the complexity of the workflow, but we favor getting a first working connection into production in weeks, not months.
Will an AI agent replace people on my team?
No. Agents take on defined, repeatable tasks and decisions; the judgment calls, exceptions, and relationship work stay with your people.
What AI platforms do you build on?
We work across Claude, OpenAI, and other major models and agent frameworks, choosing the stack based on your use case rather than a single vendor’s incentive.
Why work with us
Most agent projects stall in the pilot stage because the workflow was never the right one, or because nobody built the governance in from the start. Trellist has worked as an embedded, fractional technology partner since 1995 — long before ‘agentic AI’ was a category.
How it compares
An AI agent reasons across multiple steps, adapts to new information, and takes action across systems with minimal human input. That’s different from RPA, which follows fixed, pre-programmed rules and breaks the moment a process deviates from script — and different from a chatbot, which answers conversational questions but generally doesn’t take multi-step action across your business systems.
Why fractional, embedded teams win
- Firms running three or more AI use cases in production report roughly 4x the return of a single deployment (NMS Consulting, 2026).
- Partner-built AI succeeds at roughly twice the rate of AI built internally from scratch (MIT, 2025) — the surer bet is rarely going it alone.
FAQ
How long does it take to deploy a custom AI agent?
It depends on the complexity of the workflow, but we favor getting a first working connection into production in weeks, not months.
Will an AI agent replace people on my team?
No. Agents take on defined, repeatable tasks and decisions; the judgment calls, exceptions, and relationship work stay with your people.
What AI platforms do you build on?
We work across Claude, OpenAI, and other major models and agent frameworks, choosing the stack based on your use case rather than a single vendor’s incentive.
Why work with us
Most agent projects stall in the pilot stage because the workflow was never the right one, or because nobody built the governance in from the start. Trellist has worked as an embedded, fractional technology partner since 1995 — long before ‘agentic AI’ was a category.
Ready to put an AI agent to work on a real problem? Let’s talk about which workflow is the right place to start.
Let’s talk
We’re ready to listen
Tell us a little about yourself and our team will reach out to schedule a call.
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