AI Agent Strategy & Consulting
We review your workflows, data, and risk profile to identify which tasks are right for agentic automation, then deliver an architecture and integration roadmap tied to a defined business outcome.
Custom AI agent development designed for production and secure business workflow automation.
LuMay develops AI agents that understand requests, retrieve information, use approved tools, complete multi-step tasks, and work alongside your teams — from focused task agents to enterprise multi-agent systems, built for production, not just demos.
Standard chatbots generate content or respond to queries. AI agents execute multi-step workflows autonomously with control.
Most AI applications generate content or answer questions. AI agents go further — interpreting a goal, planning the steps to complete it, retrieving information, taking approved actions, and checking the result. A support agent, for example, can understand a request, pull account and order data, search the knowledge base, resolve or recommend a fix, update the ticket, and escalate when human judgment is needed.
Building a reliable agent takes more than connecting a language model to an interface — it needs memory, system integrations, access controls, orchestration, validation, monitoring, and human-approval mechanisms. We bring these together into solutions that are useful, controlled, and tied to measurable outcomes.
A prompt connected to a language model isn't enough for a reliable enterprise solution — production agents need all of these components.
AI agents aren't always the right fit — a simple chatbot may suit information access, and traditional automation may suit stable, predictable processes. We assess your workflow before recommending an approach.
| Capability | AI Agent | AI Copilot | Chatbot | Automation |
|---|---|---|---|---|
| Main purpose | Complete goals/workflows | Assist users | Answer questions | Execute fixed rules |
| Autonomy | Controlled/semi-autonomous | User-directed | Mostly reactive | Fully predefined |
| Multi-step tasks | Yes | Limited | Usually limited | Yes, if predictable |
| Tool use | Dynamic | User-approved | Limited | Preconfigured |
| Human involvement | Based on risk | High | Moderate | Required for exceptions |
| Best use | Variable workflows | Knowledge work | Info support | Stable processes |
We offer end-to-end development services to plan, build, integrate, and scale secure autonomous agents inside your enterprise.
We review your workflows, data, and risk profile to identify which tasks are right for agentic automation, then deliver an architecture and integration roadmap tied to a defined business outcome.
We build a focused agent around one workflow, test it against real data and success criteria, and confirm whether it can reliably do useful work before you scale it.
We design agents around your workflows and systems — able to interpret requests, retrieve approved information, call tools, hold context, produce structured outputs, seek approval, and escalate exceptions — each with a defined role, permissions, and boundaries.
Focused agents built for one job — classifying requests, extracting contract or invoice data, updating CRM records, summarizing meetings — often the easiest starting point to test, monitor, and govern.
For complex workflows, we design specialized agents (collection, analysis, validation) coordinated by a supervisor agent, with task delegation, shared state, conflict resolution, and human escalation — used only when it beats a simpler single-agent approach.
Copilots that retrieve information, summarize documents, draft content, and recommend next steps while keeping the user in control — built for employees, customers, sales, developers, and other roles.
Text and voice agents for support, scheduling, onboarding, and service requests, with speech recognition, multilingual support, and real-time system integration where needed.
Agents that coordinate across applications and teams — collecting data, applying business rules, updating systems, and routing exceptions — for onboarding, invoicing, claims, procurement, and similar processes, combined with traditional automation where full predictability is required.
Agents that retrieve from approved sources — policies, manuals, contracts, support articles — to generate accurate, context-aware answers, with attention to permissions, freshness, accuracy, and sensitive-data protection.
We connect agents to your CRM, ERP, HR, support, and analytics systems, handling authentication, permissions, retries, failure recovery, and audit logging.
Role-based access, least-privilege permissions, tool allowlists, input/output validation, prompt-injection protection, approval gates, spending limits, and full execution logs — routing sensitive actions to an authorized person before execution.
We test full workflow outcomes, not just responses — task completion, tool accuracy, edge cases, safety, and prompt-injection scenarios — to confirm the agent uses the right data, picks the right tool, and escalates when it should.
Cloud or private deployment with CI/CD, version control, rollback, and scaling, followed by ongoing monitoring of task success, errors, latency, cost, and user feedback.
Ongoing incident investigation, prompt and workflow refinement, model migration, new integrations, and security and performance tuning as your needs evolve.
We build specialized agents customized to specific departmental goals, systems, and operational boundaries.
resolve requests, update tickets, escalate complex cases
research accounts, enrich leads, keep CRM records current
gather and compare information, prepare structured reports
retrieve data, run approved analyses, explain findings
coordinate recurring cross-team tasks and approvals
invoice processing, reconciliation, exception detection
onboarding, policy questions, scheduling
triage incidents, recommend fixes, escalate unresolved issues
code review, test generation, debugging support
extract, classify, and validate contracts and forms
surface reliable answers from approved sources
coordinate availability, book and update appointments
Our engineering lifecycle focuses on security, model validation, and progressive rollout to guarantee reliability.
We define objectives, confirm fit, and outline technical security requirements.
We map out the agent models, memory states, tools, and integration paths.
We construct a lightweight prototype to validate the core workflow with real data.
We build the complete agent and connect it safely to your enterprise systems.
We run security reviews, deploy in stages, and monitor initial outcomes.
We safeguard your applications against standard LLM edge cases, loops, hallucinations, and unauthorized actions.
grounding, structured outputs, validation, human approval
timeouts, retries, fallback paths, safe termination
step limits, execution budgets, duplicate-action controls
access controls, filtering, encryption, retention policies
least privilege, tool allowlists, approval gates, logs
full logging of activity, calls, approvals, and outcomes
The process of building software that understands objectives, retrieves information, uses tools, and completes multi-step tasks — covering model selection, architecture, integrations, testing, security, and monitoring.
A chatbot mainly answers questions. An agent can also plan tasks, interact with applications, retrieve records, and complete approved workflow steps.
A copilot supports a user with information or drafts. An agent can independently perform approved actions, though sensitive tasks can still require human approval.
A single agent usually suits a focused workflow. Multiple agents help when a process has clearly separate responsibilities needing different tools or knowledge.
Yes — CRM, ERP, HR, support, document, analytics, and custom systems, wherever suitable APIs and permissions exist.
Yes, through controlled databases, APIs, or retrieval systems with appropriate access controls, encryption, and retention rules.
Through tool restrictions, structured outputs, validation, confidence thresholds, approval gates, testing, and monitoring.
Yes — an agent can prepare an action and route it to an authorized person for review before execution.
We log model requests, retrieved data, tool calls, approvals, errors, latency, cost, and final outcomes.
It depends on task complexity, accuracy needs, tool-use reliability, speed, cost, and privacy requirements — there's no single best model.
It depends on workflow complexity, integrations, and security needs. A proof of concept generally takes less time than a full multi-agent platform.
Cost depends on the agent's capabilities, architecture, integrations, security, usage, and maintenance needs.
Yes — we can evaluate and improve architecture, prompts, integrations, retrieval, reliability, and cost.
Ongoing monitoring, incident handling, model reviews, integration maintenance, security updates, and optimization.
Whether you're exploring your first AI agent, validating a proof of concept, or building an enterprise agent platform, LuMay combines strategy, secure architecture, evaluation, and deployment to build agents that support real business work.