CUSTOM AI AGENT DEVELOPMENT SERVICES
We build custom AI agents – autonomous systems powered by GPT, Claude, Gemini, or open-source models – that execute multi-step tasks across your stack. From updating CRM and ERP records to triaging tickets and generating reports, our agents plan, decide, and act within your data, your rules, and your tools – doing the work, not just answering questions.
Projects Delivered
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Years of Expertise
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AI Agent & Workflow Automation Services We Deliver
Custom AI Agent Development
We architect autonomous agents from the ground up – selecting the right foundation model, designing tool-use and function-calling schemas, and building the orchestration layer (LangGraph, CrewAI, or custom) so the agent plans, acts, and self-corrects across multiple steps.
Intelligent Workflow Automation
We map your existing processes, pinpoint high-value targets, and replace manual handoffs with AI-powered workflows – like invoice processing that reads, validates, routes, and books entries end-to-end, or onboarding flows that provision accounts and notify managers automatically.
Multi-Agent Orchestration
Complex operations need more than one agent. We build architectures where specialized agents collaborate – research, analysis, and action – with coordination, conflict resolution, and fallback logic built in so the system stays reliable at scale.
Enterprise System Integration
Agents are only as useful as the systems they can reach. We build secure, authenticated connectors to Salesforce, HubSpot, SAP, NetSuite, Jira, Slack, Teams, databases, and custom APIs – with read-write access and full audit trails.
RAG for AI Agents
We equip agents with RAG pipelines so they reason over your proprietary knowledge – wikis, policies, product catalogs, historical tickets – not generic model memory. Vector stores, hybrid search, and re-ranking make sure the right context is retrieved every time.
Human-in-the-Loop & Guardrails
Full autonomy isn't always the right day-one setting. We design configurable approval gates, confidence thresholds, and escalation paths so agents operate within boundaries you define – and humans stay in the loop exactly where it matters.
Technologies Behind Our AI Agents
We pick models and frameworks for each agent based on task complexity, cost, and data-privacy needs, and build on open standards – so you can swap components as the technology evolves.
Foundation Models
OpenAI GPT, Anthropic Claude, and Google Gemini for complex reasoning and tool use; open-weight models such as Llama, Mistral, and Qwen where cost, latency, or data residency calls for self-hosting.
Infrastructure & Deployment
Docker and Kubernetes on AWS, Azure, or Google Cloud, provisioned with Terraform and shipped through CI/CD; PostgreSQL with pgvector or Redis for agent memory and state. On-premise and air-gapped deployments for regulated industries.
Observability, Evaluation & Guardrails
LangSmith, Langfuse, and OpenTelemetry for step-level tracing; promptfoo and DeepEval for regression and adversarial tests; NeMo Guardrails and Guardrails AI for approval gates, output validation, and prompt-injection defense.
Agent Frameworks & Protocols
LangGraph, CrewAI, AutoGen, and vendor agent SDKs for planning and multi-agent coordination, with Model Context Protocol (MCP) servers exposing your tools and the Agent2Agent (A2A) protocol for agent-to-agent communication.
Workflow & Integration Layer
Temporal and Apache Airflow for durable, retryable multi-step execution; n8n, Camunda, and UiPath where agents plug into existing automation; REST, GraphQL, webhooks, and SSO connect agents to Salesforce, SAP, NetSuite, Jira, and Slack.
How We Build Your AI Agents
Our delivery process mirrors the proven workflow we use across all AI engagements – adapted for the iterative testing and safety controls autonomous agents demand.
Discovery & Workflow Mapping
We audit your current processes, identify automation candidates, and score them by ROI potential, data readiness, and risk. We also map every system the agent needs to touch. You walk away with a prioritized agent roadmap.
Architecture & Prototype
We define the agent’s goals, tool inventory, memory strategy, and guardrails – plus inter-agent protocols for multi-agent systems. A working prototype connected to your real systems follows within weeks, so stakeholders can test before we commit to the full build.
Build, Integrate & Harden
Iterative two-week sprints, each delivering a working increment connected to real systems. Automated evaluations, adversarial testing, and edge-case simulations track task-completion rate, latency, cost-per-run, and hallucination rate before anything reaches production.
Deploy & Continuous Monitoring
Production rollout with token-level tracing, step-by-step execution logs, and real-time dashboards. We provide runbooks and on-call support through the stabilization window. Every action is traceable, so you always see what each agent did and why.
Industries & Use Cases
Our AI agents serve organizations across sectors – wherever multi-step, cross-system work slows teams down and manual handoffs create errors and delays.
Wholesale & Distribution
Purchasing agents that process supplier orders, chase confirmations, reconcile inventory, and update your ERP – with approval gates for high-value orders.
Insurance
Claims-intake agents that request missing documents, check coverage in the policy system, and route each claim to the right adjuster.
Telecommunications
Customer-care agents that resolve billing disputes and provision services through your BSS/OSS APIs, escalating complex cases to a human with full context.
IT & Managed Services
Incident-response agents that detect anomalies, pull logs, run diagnostics, and either auto-remediate or open a pre-populated ticket for the on-call engineer.
Frequently Asked Questions
Everything you need to know before building an AI agent.
How long does it take to build and launch an AI agent?
We can kick off within 1–2 weeks. We work in two-week sprints, and each one delivers a working increment connected to your real systems, so your team can test early. Total production time depends mainly on how many systems the agents touch and how complex the workflow is. You get a clear timeline after discovery.
Can AI agents work with our own data and connect to our systems?
Yes. We build secure, authenticated connectors to your CRM, ERP, project-management tools, databases, and custom APIs, and use RAG pipelines so agents reason over your internal documents instead of generic model knowledge. Every read and write action is logged with a full audit trail. On-premise and air-gapped deployments are available for regulated industries.
How do you keep AI agents accurate and under control?
We design approval gates, confidence thresholds, and escalation paths so humans stay in the loop where it matters. Before launch, we run automated evaluations and adversarial testing that measure task-completion rate, hallucination rate, and cost per run – and after launch, tracing and dashboards show every step an agent takes.