Solutions
AI agents that run real business work
An AI agent is software that reads a request, decides what to do, calls the systems it needs, and reports the result. Lingows AI builds agents that work inside a company's existing CRM, help desk, and data stores, then keeps those agents running after launch.
01What this is
An AI agent combines a language model with tools, memory, and guardrails. The model handles judgment. The tools perform the actual work: reading a ticket, querying a database, writing a record, sending a reply. Guardrails decide what the agent may do without a human.
Lingows AI builds agents against production systems from the first week. An agent that only works on sample data is a demo. The build target is a narrow scope of work the agent handles end to end, with a clear escalation path for anything outside that scope.
02Who this suits
High volume, repeatable decisions
Work that arrives constantly and follows a pattern, such as tier one support, lead qualification, or invoice triage.
Teams blocked by headcount
Operations that cannot scale without hiring, where the constraint is throughput rather than skill.
Companies with real systems of record
An agent needs somewhere to read and write. A CRM, ERP, help desk, or database makes the work concrete.
Organizations willing to define escalation
Agents work best where a company can state plainly which cases must reach a person.
03How it works
- Step 01
Scope one workflow
Pick a single decision that repeats. Write down the inputs, the systems involved, the acceptable outputs, and the cases that must escalate.
- Step 02
Build tools before prompts
Every action the agent can take becomes a typed tool with validation. The model chooses among tools. It never writes raw queries against production.
- Step 03
Evaluate against real history
Replay past cases through the agent and compare its output to what actually happened. That set becomes the regression suite.
- Step 04
Ship behind a human
The agent drafts, a person approves. Approval rates and correction patterns decide when the human step comes out.
04Effort and timeline
| Item | Typical |
|---|---|
| Typical first agent | 4 to 8 weeks from scope to production |
| Client time required | 2 to 4 hours per week during build |
| Systems touched | 1 to 3 at launch, expanded after |
| Escalation design | Required before launch, not after |
| Ongoing operation | Monitoring and tuning under managed AI operations |
05Common failure modes
Scope with no boundary
An agent asked to handle anything handles nothing reliably. Narrow scope with clean escalation beats broad scope with vague fallback.
No evaluation set
Without replayed history there is no way to tell whether a prompt change improved or degraded behavior.
Write access granted too early
Agents that write to production before approval rates are known create cleanup work that exceeds the time saved.
Nobody owns it after launch
Models change, systems change, and volume shifts. An unowned agent degrades quietly.
CAPAI Agents capabilities
Customer Service Agents
An AI customer service agent reads an incoming ticket, retrieves the approved answer, takes the action the ticket requires, and escalates anything outside its scope. Lingows AI builds these agents against a live help desk so resolution happens in the system support staff already use.
Sales Development Agents
A sales development agent responds to inbound leads within minutes, asks qualifying questions, records the answers as structured CRM fields, and books time with a person when the lead fits. Lingows AI builds these agents around a company's own qualification criteria rather than a generic script.
Research Agents
A research agent gathers information from approved sources, extracts it into a fixed structure, and cites every claim it makes. Lingows AI builds research agents for competitive monitoring, due diligence preparation, and internal analysis where the output has to be checkable rather than merely fluent.
Operations Agents
An operations agent handles internal work that arrives constantly and follows a pattern: request triage, record updates, status chasing, and routine reconciliation. Lingows AI builds operations agents that work across the systems a back office already uses and escalate anything that falls outside defined rules.
FAQQuestions
Common questions
How is an AI agent different from a chatbot?
A chatbot answers questions. An agent takes actions in other systems, such as updating a record, scheduling a task, or issuing a refund, and reports what it did.
Do AI agents replace staff?
In most Lingows engagements agents absorb repeated volume so staff handle exceptions and higher value work. The common outcome is faster response time rather than reduced headcount.
What does an AI agent cost to run?
Cost is model usage plus operations. Usage scales with volume and with how much context each request carries. Cost control is a design decision made during the build, not a later fix.
Last reviewed 2026-08-16
NEXTRelated pillars
- Agentic Workflow Automation
Agentic workflow automation chains several AI steps and system calls into one reliable process. A workflow reads an input, routes it, calls the tools it needs, pauses for human approval where required, and records every step. Lingows AI designs, builds, and operates these workflows in production.
- Custom AI Chatbots
A custom AI chatbot answers from a company's own content and connects to that company's systems. Lingows AI builds chat for websites, phone lines, messaging channels, and internal knowledge bases, with retrieval grounded in approved sources and a defined handoff to a person.
Scope a ai agents build
Bring one process and its numbers. Lingows AI will tell you what is worth automating, what is not, and what running it in production costs.