Build, own, and deploy autonomous AI agents — on your infrastructure, with your data, under your control.
Custom AI agent development means building agents that reason, plan, and act — not just respond. On ibl.ai, your team defines agent roles, connects live data sources, and deploys production-grade agents without waiting on a vendor roadmap.
These are not chatbots. ibl.ai agents execute multi-step workflows, call external APIs, run code, and make decisions based on context — all within a platform your organization fully owns.
With 1.6M+ users across 400+ organizations — including NVIDIA, Kaplan, and Syracuse University — ibl.ai delivers the infrastructure, tooling, and source code to make custom agent development a core enterprise capability, not a one-off experiment.
Most enterprises that adopt AI agents end up dependent on a vendor's black box. They can configure, but not build. They can prompt, but not extend. When business requirements change, they wait — for a feature release, a pricing tier upgrade, or a support ticket response.
The deeper problem is ownership. When your agents live in a vendor's cloud, your workflows, your logic, and your data are all subject to someone else's terms. Outages, deprecations, and price changes become your operational risk. Custom agent development solves this — but only when the platform gives you the full stack.
Most AI platforms expose limited configuration options. You can adjust prompts and toggle features, but the underlying agent behavior is owned and controlled by the vendor.
Your agents cannot be meaningfully customized. Business-critical workflows are constrained by what the vendor chose to expose.SaaS AI platforms rarely provide source code. Your team cannot inspect, modify, or extend the agent runtime — making deep customization impossible.
Engineering teams are blocked from building differentiated capabilities. Every custom requirement becomes a vendor negotiation.Connecting agents to internal systems — databases, ERPs, document stores — typically requires routing data through vendor infrastructure, creating compliance and security exposure.
Sensitive data leaves your perimeter. Regulated industries face audit failures, and security teams block deployment entirely.Many agent platforms are tightly coupled to a single LLM provider. Switching models — for cost, performance, or compliance reasons — requires rebuilding from scratch.
Organizations are trapped paying premium model pricing with no leverage, and cannot adopt newer or domain-specific models as they emerge.When an AI agent takes an action — sends a message, updates a record, calls an API — most platforms provide no structured log of what happened, why, or who authorized it.
Compliance teams cannot review agent behavior. Incidents cannot be investigated. Regulated deployments are blocked entirely.Use ibl.ai's agent builder to define the agent's purpose, persona, decision boundaries, and escalation rules. Assign roles within multi-agent workflows where agents collaborate or hand off tasks.
Use the Model Context Protocol (MCP) to connect agents to internal databases, document repositories, APIs, and enterprise systems — without routing data through external infrastructure.
Equip agents with built-in tools: code execution, API calls, web search, file parsing, and form submission. Define which tools each agent can use and under what conditions.
Design multi-step agent workflows using ibl.ai's workflow engine. Test agent behavior against real data in a sandboxed environment before promoting to production.
Deploy agents to your own cloud, on-premises servers, or air-gapped environment. ibl.ai runs entirely on your infrastructure — no external dependencies required.
Every agent action is logged in a complete audit trail. Review decisions, trace reasoning chains, monitor performance, and push updates — all through the API-first platform your team owns.
Customers receive the complete ibl.ai codebase. Your engineering team can inspect, modify, and extend every layer of the agent runtime — no black boxes, no permission requests.
Deploy agents powered by Claude, GPT-4, Gemini, Llama, Mistral, or your own fine-tuned models. Swap models without rebuilding agent logic — the runtime is fully decoupled from the LLM layer.
The Model Context Protocol connects agents to live enterprise data sources — databases, APIs, document stores, and internal tools — with structured, auditable data access.
Every agent action, decision, tool call, and API request is logged with full context. Compliance teams can review, export, and report on agent behavior at any granularity.
Build networks of specialized agents that collaborate, delegate, and hand off tasks. Define orchestration logic, fallback paths, and human-in-the-loop checkpoints.
Run the entire agent platform — including LLM inference — inside a fully isolated environment with zero external network dependencies. Designed for classified, regulated, and high-security deployments.
Every agent capability — creation, execution, monitoring, configuration — is accessible via RESTful APIs. Integrate agent development into existing CI/CD pipelines and enterprise toolchains.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Source Code Access | Vendor retains all source code. Your team configures within exposed limits and submits feature requests that may never ship. | ibl.ai delivers the complete codebase. Your engineers own, modify, and extend every layer of the agent runtime on day one. |
| Infrastructure Control | Agents run on vendor cloud. Outages, data residency violations, and pricing changes are outside your control. | Agents run entirely on your infrastructure — cloud, on-premises, or air-gapped. No external dependencies. No vendor uptime risk. |
| Model Flexibility | Locked to the vendor's preferred LLM. Switching models requires rebuilding agent logic or negotiating a new contract tier. | Model-agnostic runtime supports Claude, GPT, Gemini, Llama, Mistral, or custom fine-tuned models. Swap without rebuilding workflows. |
| Audit and Compliance | Agent actions are opaque. No structured log of decisions, tool calls, or data accessed. Compliance teams cannot review or report on agent behavior. | Every agent action is logged in a complete, structured audit trail. Compliance teams can query, export, and report on any agent interaction. |
| Customization Depth | Customization is limited to prompt tuning and feature toggles. Business-specific logic requires vendor professional services engagements. | Full source code ownership means unlimited customization depth. Your team builds domain-specific tools, workflows, and agent behaviors without vendor involvement. |
| Vendor Dependency | Platform shuts down if vendor is acquired, pivots, or raises prices. Your workflows and data are held hostage to the vendor relationship. | ibl.ai runs independently on your infrastructure. The platform continues operating regardless of any changes to the vendor relationship. |
| Data Security | Connecting agents to internal systems requires routing data through vendor infrastructure, creating compliance exposure and security risk. | MCP connections are internal by design. Sensitive data never leaves your perimeter. Air-gapped deployment eliminates all external data transmission. |
Vendor retains all source code. Your team configures within exposed limits and submits feature requests that may never ship.
ibl.ai delivers the complete codebase. Your engineers own, modify, and extend every layer of the agent runtime on day one.
Agents run on vendor cloud. Outages, data residency violations, and pricing changes are outside your control.
Agents run entirely on your infrastructure — cloud, on-premises, or air-gapped. No external dependencies. No vendor uptime risk.
Locked to the vendor's preferred LLM. Switching models requires rebuilding agent logic or negotiating a new contract tier.
Model-agnostic runtime supports Claude, GPT, Gemini, Llama, Mistral, or custom fine-tuned models. Swap without rebuilding workflows.
Agent actions are opaque. No structured log of decisions, tool calls, or data accessed. Compliance teams cannot review or report on agent behavior.
Every agent action is logged in a complete, structured audit trail. Compliance teams can query, export, and report on any agent interaction.
Customization is limited to prompt tuning and feature toggles. Business-specific logic requires vendor professional services engagements.
Full source code ownership means unlimited customization depth. Your team builds domain-specific tools, workflows, and agent behaviors without vendor involvement.
Platform shuts down if vendor is acquired, pivots, or raises prices. Your workflows and data are held hostage to the vendor relationship.
ibl.ai runs independently on your infrastructure. The platform continues operating regardless of any changes to the vendor relationship.
Connecting agents to internal systems requires routing data through vendor infrastructure, creating compliance exposure and security risk.
MCP connections are internal by design. Sensitive data never leaves your perimeter. Air-gapped deployment eliminates all external data transmission.
Zero data exfiltration risk. Full audit trail for every agent action. Deployable in classified environments with no external dependencies.
PHI never leaves the organization's perimeter. Agents accelerate clinical workflows while maintaining full regulatory compliance.
Structured audit logs satisfy regulatory requirements. Model-agnostic runtime allows rapid adoption of newer, cost-efficient models without workflow disruption.
Sensitive client data stays on firm infrastructure. Agents reduce document review time while maintaining attorney oversight at defined checkpoints.
Agents operate on-premises at remote facilities with no cloud dependency. Real-time action logging supports safety and regulatory audit requirements.
End-to-end workflow automation reduces manual coordination overhead. Full source code ownership allows deep integration with proprietary ERP and MES systems.
Agents reduce claims processing time and error rates. Complete audit trail supports dispute resolution and regulatory examination.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.