AgentHQ

Deployable AI Agents and Maintenance

We build secure AI agents and deploy them inside your environment, so your data stays with you. Your team runs the agent day to day. We maintain it under a monthly retainer.

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The Problem

Why Generic AI Tools Fall Short for Enterprise

Off-the-shelf AI products are built for general use. They are not built for your compliance obligations, your data structure, your workflows, or the regulatory context your industry operates in.

Enterprise organizations in healthcare, finance, insurance, and government need AI systems that are customized to their specific processes, integrated with their existing infrastructure, and built with the safety controls that regulated environments require.

That is not something a SaaS subscription delivers. It requires engineering.

Generic Tools, Compliance Gaps
Consumer AI tools lack the audit trails, access controls, and data handling standards that regulated industries require.
No Integration with Your Systems
Off-the-shelf AI cannot connect deeply with your CRM, ERP, or internal databases. The result is manual data transfer and workflow fragmentation.
Safety Not Designed In
Platform AI tools are not built with your specific risk scenarios in mind. Safety controls need to match your actual risk profile.
No Accountability Structure
When a platform agent makes an error, there is no clear owner, no escalation path, and no documented process for remediation.
What We Build

Custom AI Systems Built Around Your Operations

Every agent we build is designed for your specific workflows, your data, and your compliance context. Nothing generic. Nothing reused from a previous client.

Custom AI Agent Development

We design and build AI agents that are specific to your business processes. From single-purpose agents that handle one task to multi-agent systems that coordinate complex workflows, every agent is built around how your organization actually operates.

Business-Specific AI Agents
Agents designed around your actual workflows and decision processes, not adapted from generic templates.
Multi-Agent Architecture
Systems of specialized agents that work together with clear handoff protocols and failure handling between agents.
LLM Pipeline Design
Structured pipelines that control how information flows through your AI system for consistent, reliable outputs.
n8n Workflow Integration
Automated workflow orchestration that connects your AI agents to business processes and external systems.
MCP Integration
Model Context Protocol implementation for standardized, reliable connections between AI models and your enterprise tools.
Agent Orchestration
Coordination logic that manages task routing, priority handling, and agent communication in complex multi-agent systems.

Safety and Guardrails

Safety is not a layer we apply after building. It is the structural foundation we design around. Every agent includes input validation, output controls, and behavior boundaries specific to your risk profile.

Prompt Injection Protection
Input sanitization and trust boundary enforcement that prevents adversarial inputs from hijacking agent behavior.
Output Validation
Structured validation schemas that check agent responses against defined standards before they reach users or downstream systems.
Behavior Boundaries
Defined constraints that prevent agents from taking actions outside their intended scope, regardless of what input they receive.
Confidence Thresholds
Automatic escalation to human review when an agent’s confidence falls below acceptable thresholds for a given task.
Data Exposure Controls
Filtering and redaction logic that ensures sensitive data cannot appear in agent outputs to unauthorized parties.
Failure Mode Design
Explicit handling for edge cases and system failures that defaults to safe states rather than unpredictable outcomes.

Agent Testing

We test AI agents with the same rigor we apply to mission-critical software, plus AI-specific testing methods that standard QA does not cover. No system ships without passing our full test suite.

Red Team Testing
Adversarial testing that specifically probes for prompt injection, jailbreak attempts, and edge case failures that routine testing misses.
Accuracy Evaluation
Systematic evaluation of agent output accuracy against defined benchmarks for your specific use case and domain.
Performance Testing
Load testing under realistic volume conditions to ensure performance is consistent at enterprise scale.
Integration Testing
End-to-end testing of all system connections and data flows between the agent and your enterprise systems.
User Acceptance Testing
Structured testing with your team against real workflows before any system is deployed to production.
Regression Testing
Ongoing testing protocols that verify system behavior remains consistent after model updates or configuration changes.

Enterprise Integration Services

An AI agent that cannot access your data and systems is limited in what it can do. We build deep integrations with your existing infrastructure so your agents work with the data and systems your organization already relies on.

CRM Integration
Salesforce, HubSpot, Microsoft Dynamics, and other CRM platforms.
ERP Integration
SAP, Oracle, Microsoft Dynamics, and other ERP systems.
API Integration
REST and GraphQL APIs, webhooks, and real-time data streams.
Knowledge Base Integration
Vector databases, document repositories, and internal knowledge systems.
Database Access
Secure, controlled read and write access to your internal databases with full audit logging.
Identity and SSO
Integration with your identity provider for seamless, secure user authentication and authorization.

Human-in-the-Loop Design

Human oversight is not an obstacle to AI efficiency. It is what makes AI safe to deploy in enterprise contexts. We design human review into the right places so it functions as a safety net rather than a bottleneck.

Approval Workflows
Structured approval steps for actions that require human authorization before the agent can proceed.
Escalation Systems
Automatic escalation to appropriate human reviewers when agent confidence or task complexity exceeds defined thresholds.
Manual Review Queues
Organized queues with structured context so reviewers can process exceptions efficiently without losing information.

Maintenance and Monitoring

AI systems require ongoing attention after deployment. Models change, business requirements evolve, and usage patterns shift. We provide structured post-deployment support so your system continues to perform as designed.

Performance Monitoring
Continuous monitoring of accuracy, latency, and error rates with alerting when performance falls below acceptable thresholds.
Drift Detection
Automated detection of performance drift that can occur as underlying models are updated by providers.
Ongoing Optimization
Regular review and tuning of agent prompts, configurations, and workflows based on production performance data.
What You Receive

Deliverables at Project Completion

At the end of every engagement, you receive a complete, documented, production-ready system that belongs entirely to you.

Deployed AI System
Working system deployed in your infrastructure. You own the code, the configuration, and the system entirely.
Complete Source Code
Full source code with documentation. No lock-in. No ongoing licensing fees for the system we build.
Architecture Documentation
Detailed architecture diagrams, data flow documentation, and system design records for your technical team.
Governance Documentation
Policies, model documentation, access control records, and decision logs suitable for regulatory review.
Test Reports
Full testing documentation including red team results, accuracy evaluation, performance benchmarks, and integration test reports.
Team Training and Handoff
Training sessions for your team, operational runbooks, and a 90-day post-deployment support commitment.
Questions

Common Questions About AI Agent Engineering

An AI agent is an autonomous system that can perform multi-step tasks, make decisions, use tools, access data, and take actions on your behalf. A chatbot typically answers questions in a conversation. An AI agent can research a topic, draft a document, check a database, send a notification, update a record, and escalate to a human when needed. The difference is autonomy, capability, and integration depth.
Timeline depends on complexity. A focused single-agent system can be designed, built, tested, and deployed in eight to twelve weeks. A multi-agent system with deep enterprise integrations typically takes four to six months. We provide a detailed timeline in the architecture phase before any development begins.
Yes. We have experience integrating AI agents with Salesforce, HubSpot, SAP, Oracle, Microsoft Dynamics, ServiceNow, and many other enterprise platforms. Integration complexity depends on your system’s API maturity and data quality. We assess integration requirements during the discovery phase and include all integration work in the project scope.
Prompt injection is one of the most significant security risks in AI agent systems. We address it through input sanitization layers, output validation schemas, trust boundary enforcement, and behavioral testing. We conduct red team sessions specifically designed to probe for injection vulnerabilities, adversarial inputs, and unexpected outputs. Security testing is part of every agent we build.
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Get Started

Ready to Build AI Agents That Actually Work?

Start with a 30-minute discovery call. We will listen to what you are trying to build and tell you honestly what the right path looks like.

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