AI Agents
Custom AI agents that don’t just answer questions. They get work done.
Most chatbots don’t do more than answer questions. We design, build and run custom AI agents that get work done: they understand context, call your systems, make decisions within the limits you set, and hand off to a person when they’re not sure. That means less copy-and-paste work, faster turnaround and a full audit trail.
- Agentic workflows
- Multi-agent orchestration
- MCP & tool use
- Human-in-the-loop
Anatomy of an agent
Five layers we build into every agent
An agent is only as good as the system around the model, so we engineer each layer on purpose.
- L1
Goal & policy
The job to be done, what success looks like, and the rules the agent must never break.
- L2
Reasoning model
The right LLM for the task, balanced for accuracy, speed and cost, with structured planning.
- L3
Memory & knowledge
Retrieval over your documents and records, plus short- and long-term memory for the task at hand.
- L4
Tools & actions
Secure, permissioned access to your CRM, ERP, ticketing, email and APIs through MCP servers and function calling.
- L5
Guardrails & observability
Approval gates, confidence thresholds, a full trace of every step, and evals that catch regressions before your customers do.
Agents we build
Proven agent patterns for business
- Support
Customer service agents
Resolve tickets end to end: understand the request, look up the order, issue refunds within policy, and escalate with full context when needed.
- Operations
Back-office process agents
Invoice matching, onboarding, claims triage and reconciliations that run around the clock across your systems.
- Revenue
Sales & revenue agents
Lead research, qualification, meeting prep and CRM cleanup, so your sales team can spend its time selling.
- Voice
Voice agents
Inbound and outbound calls in English, Spanish and other languages for scheduling, reminders and first-line support.
- Dev
Engineering agents
Code review, test generation, incident triage and documentation agents that plug into your development workflow.
- Orchestration
Multi-agent systems
Specialist agents coordinated by a planner for complex, multi-step work, with clear hand-offs between them.
Delivery
From your first agent to an agent workforce
- 01
Week 1
Map the workflow
We shadow the process, identify the decisions and systems involved, and agree on where a person must sign off.
- 02
Weeks 2–5
Build the first agent
Tools, prompts, memory and evals on your real data, running in shadow mode alongside your team.
- 03
Weeks 6–8
Go live with guardrails
Autonomy grows in stages, from suggest to approve to act, with dashboards for accuracy, cost and throughput.
- 04
Ongoing
Expand
We add agents and hand-offs, reuse shared tools and memory, and keep improving from what we see in production.
FAQ
AI Agents: FAQs
Can’t find what you’re looking for? Book a call and ask us directly.
A chatbot answers questions. An AI agent works toward a goal. It plans the steps, calls your tools and systems, checks the results and completes a task, like updating a record, sending a quote or resolving a ticket, all within the permissions and guardrails you define.
We give agents only the access they need, check policy before any action, route uncertain cases to a person, require approval for sensitive steps, and keep a full trace of every decision for auditing. We only increase autonomy as measured accuracy proves it’s ready.
Yes. Agents connect through APIs, Model Context Protocol (MCP) servers, RPA where there’s no API, and native platforms like Salesforce Agentforce. Building and securing those connectors is part of the engagement.
We choose based on the use case. That includes the OpenAI and Anthropic agent SDKs, LangGraph, CrewAI, Semantic Kernel, Amazon Bedrock Agents and Salesforce Agentforce. We keep the business logic portable so you’re never locked into one framework.
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