Custom AI Agent Development
Intelligent Automation for Business
40% of enterprise apps will include AI agents by 2026 (Gartner). We build the support agents, sales agents, voice agents, multi-agent systems, and workflow agents that make that prediction real. LangChain, CrewAI, AutoGen — production-grade, cost-optimized, and safe.
AI Agents We Build
Six categories of AI agent, each with different architecture requirements, safety considerations, and integration patterns.
Customer Support Agents
AI agents that resolve Tier-1 and Tier-2 support tickets by searching your knowledge base, understanding conversation context, and taking actions in your systems. They don't just answer questions — they reset passwords, process refunds, update orders, and escalate to humans when they hit the boundary of their authority.
Measured results: Typical results: 60-70% ticket deflection, 8-second average response time (down from 4 hours), 90%+ customer satisfaction on AI-handled interactions.
Sales & Lead Qualification Agents
Agents that engage website visitors, qualify leads by asking the right questions, book meetings on your calendar, and push qualified opportunities into your CRM. They work 24/7, never miss a follow-up, and learn which qualification questions predict high-value deals.
Measured results: Typical results: 3x more leads qualified per day, 40% reduction in time-to-first-response, automatic CRM enrichment with conversation context.
Voice AI Agents
Phone-based and in-app voice agents using Whisper, ElevenLabs, and Deepgram. Real-time speech-to-text, natural language understanding, and text-to-speech in a single low-latency pipeline. We build the voice interface, interruption handling, and fallback-to-human logic that makes voice AI usable in production.
Measured results: Typical results: sub-2-second response latency, 85%+ intent recognition accuracy, automated call handling for appointment booking, order status, and FAQ resolution.
Multi-Agent Systems
Orchestrated teams of specialized agents that collaborate on complex tasks. A research agent gathers data, an analysis agent interprets it, a writing agent drafts a report, and a QA agent reviews the output. We design the agent graph, communication protocols, error handling, and human-in-the-loop checkpoints.
Measured results: Typical results: 10x throughput on research-heavy tasks, consistent output quality through agent specialization, automatic retry and fallback when individual agents fail.
Workflow Automation Agents
Agents that sit inside your existing workflows and handle the cognitive tasks humans used to do manually. Invoice processing, contract review, data extraction, categorization, and routing. They connect to your email, Slack, CRM, and databases through tool-calling — not brittle rule-based automation.
Measured results: Typical results: 80% reduction in manual processing time, 95%+ accuracy on structured data extraction, automatic escalation for edge cases.
Research & Analysis Agents
Agents that monitor data sources, aggregate information, and produce actionable insights. Competitive intelligence, market research, regulatory monitoring, and patent landscape analysis. We build the data collection pipelines, relevance filtering, and summarization chains that turn information overload into focused briefs.
Measured results: Typical results: 20 hours of manual research compressed to 15 minutes, daily automated briefs with citation links, configurable alert thresholds.
Agent Architecture
Every agent we build has six architectural layers. Understanding them helps you evaluate what you're buying — and what questions to ask any vendor.
LLM Reasoning Core
The agent's brain — an LLM (GPT-4, Claude, or open-source) that interprets instructions, reasons about context, and decides which actions to take. We engineer the system prompts, few-shot examples, and chain-of-thought patterns that make agents reliable, not just impressive in demos.
Memory & Context
Short-term memory (conversation history), long-term memory (vector store of past interactions and documents), and working memory (current task state). We implement sliding window context management so agents don't lose track mid-conversation, even on complex multi-turn interactions.
Tool Calling Layer
The interface between the agent and your systems. We define typed tool schemas (JSON) that the agent can call: search_knowledge_base, create_ticket, send_email, update_crm. The agent reasons about which tool to use, validates parameters, and handles tool failures gracefully.
Orchestration & Routing
For multi-agent systems: the coordination layer that routes tasks between agents, manages dependencies, handles parallel execution, and implements retry/escalation logic. We use LangGraph for complex state machines and custom orchestrators for simpler flows.
Safety & Guardrails
Input validation (prompt injection detection), output filtering (PII masking, content policy enforcement), action limits (max spend per agent, restricted tool access per role), and human-in-the-loop gates for high-stakes decisions. Every agent ships with an audit log of decisions and actions.
Monitoring & Analytics
Real-time dashboards showing agent performance: resolution rate, average handling time, cost per interaction, escalation rate, and user satisfaction. We track every LLM call, tool invocation, and decision point so you can debug agent behavior and optimize over time.
Framework Comparison
There is no "best" framework — only the right one for your use case. Here's our honest assessment.
LangChain / LangGraph
Best For
Most production agent builds. Flexible, well-documented, large ecosystem.
Strength
Tool calling, structured output, conversation memory, state machines via LangGraph.
Trade-off
Abstraction overhead for simple use cases. We use it when the agent has 3+ tools or multi-step reasoning.
CrewAI
Best For
Multi-agent teams with distinct roles (researcher, writer, reviewer, etc.).
Strength
Role-based agent definition, automatic task delegation, built-in collaboration patterns.
Trade-off
Less control over individual agent behavior. Best when agents have clear, non-overlapping roles.
AutoGen (Microsoft)
Best For
Code generation agents and developer-facing tools.
Strength
Strong code execution sandbox, multi-agent conversation patterns, enterprise backing.
Trade-off
Heavier setup, more opinionated architecture. Best for code-centric use cases.
Custom (No Framework)
Best For
Simple agents with 1-2 tools, or when you need maximum control and minimal dependencies.
Strength
No framework overhead, easiest to debug, smallest attack surface.
Trade-off
You build everything yourself — memory management, tool routing, error handling. Only worth it for simple agents.
ROI Projection
Based on our client data. Assumes $50/hour fully-loaded employee cost. Your numbers will vary — we'll build a custom projection during the scoping call.
| Use Case | Before (manual) | After (agent) | Monthly Savings | Payback |
|---|---|---|---|---|
| Customer support | 160 hrs/mo | 50 hrs/mo | $5,500/mo | 1-2 months |
| Lead qualification | 80 hrs/mo | 10 hrs/mo | $3,500/mo | 2-3 months |
| Data processing | 120 hrs/mo | 15 hrs/mo | $5,250/mo | 1-2 months |
| Research & analysis | 60 hrs/mo | 8 hrs/mo | $2,600/mo | 3-4 months |
AI Agent Pricing
Fixed prices based on complexity, not hours. AI API costs are billed separately by the provider.
Single Agent
One focused agent solving one problem.
Agent Team
Multi-agent system with orchestration.
Enterprise
Full-scale intelligent automation.
AI Agent Engineering Resources
Deep dives into agent architecture, prompt injection defense, multi-agent orchestration, cost control, and production safety patterns.
Explore the AI HubAI Agent Architecture Playbook (PDF)
Our internal playbook for designing production AI agents: architecture patterns, safety checklist, framework selection guide, and cost projection template.
Proven Results
Real projects. Real numbers. See what we delivered.
AI Support Agent: Resolving 73% of Tickets Without Human Intervention
73% ticket auto-resolution, 4hr → 8min response time
An AI customer support agent that handles Tier 1 tickets via chat and email, resolves 73% automatically, and escalates the rest with full context to human agents.
AI Voice Agent: Automated Appointment Booking via Phone
Missed calls reduced from 40% to 3%, 120 appointments/month booked by AI
An AI phone agent that handles inbound calls for a dental practice, books appointments, answers FAQs, and reduces missed calls from 40% to 3%.
AI Sales Agent: Automated Lead Qualification and Meeting Booking
Lead response time: 4 hours → 90 seconds, qualified meetings up 2.4x
An AI sales development rep that qualifies inbound leads via chat and email, scores them using BANT criteria, and books meetings directly on reps' calendars.
Frequently Asked Questions
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