People collaborating at a conference table with pens and documents illustrating enterprise team workflows enabled by AaaS — Photo by Dylan Gillis on Unsplash

What Is Agent-as-a-Service (AaaS)?

Q: What is Agent-as-a-Service (AaaS)?

AaaS is a cloud delivery model where AI agent capabilities are provided as a managed service. Organizations subscribe to a platform that hosts, runs, and maintains the agent infrastructure, allowing teams to configure and deploy agents without managing servers, LLM API keys, or orchestration code themselves.

Q: What is the difference between AaaS and building agents with a framework?

An agent framework like LangChain gives you the building blocks to construct a custom agent, but you are responsible for hosting, scaling, monitoring, and maintaining it. An AaaS platform manages all of that infrastructure for you. The trade-off is less flexibility and customization in exchange for faster deployment and lower operational overhead.

Q: Which companies offer Agent-as-a-Service platforms?

Notable AaaS platforms include Lindy AI (consumer and business workflow automation), Relevance AI (enterprise knowledge work agents), Moveworks (enterprise IT and HR service agents), and Zapier AI (workflow automation with agent capabilities). Each platform targets different use cases and technical sophistication levels.

Quick Definition#

Agent-as-a-Service (AaaS) is a deployment model where AI agent capabilities are delivered as a managed cloud service. Rather than building, hosting, and maintaining agent infrastructure in-house, organizations subscribe to a platform that provides pre-built or configurable agents ready to deploy. The AaaS provider manages the underlying LLM integrations, orchestration engine, memory systems, tool connectors, and scaling infrastructure. The customer configures the agent's behavior, connects it to their data sources, and monitors outcomes through a dashboard.

AaaS sits at the intersection of Software-as-a-Service (SaaS) and AI agent technology. Just as SaaS moved enterprise software from on-premise installations to subscription cloud products, AaaS is moving AI agent deployment from bespoke engineering projects to configured service subscriptions.

To understand the underlying technology being delivered as a service, read What Are AI Agents? and AI Agent Orchestration. Browse the full AI Agents Glossary for foundational terms.

Why AaaS Has Emerged#

Building a production-grade AI agent from scratch requires a meaningful engineering investment. Teams must integrate one or more LLM APIs, implement tool-calling logic, design memory and context management, build observability instrumentation, handle errors and retries, manage security and data governance, and maintain the entire stack as LLM APIs and underlying models evolve. For many organizations — particularly those without deep ML engineering capacity — this is an expensive and risky undertaking.

AaaS platforms absorb most of that complexity. They provide:

Pre-integrated LLM backends with model selection and fallback handling
Tool libraries covering common integrations (email, calendar, CRM, ticketing, databases)
No-code or low-code configuration interfaces for non-engineers
Built-in memory and context management
Monitoring dashboards for agent activity and performance
Enterprise security controls including SSO, role-based access, and audit logging

The result is a dramatically shorter path from business requirement to deployed agent.

Categories of AaaS Platforms#

AaaS platforms are not monolithic. They vary significantly in target audience, use case focus, and technical flexibility.

Workflow Automation Platforms with Agent Capabilities#

Platforms like Zapier and Make (formerly Integromat) have extended their workflow automation products with AI agent capabilities. These platforms are best suited for teams that want to automate multi-step workflows with conditional logic and tool calls but do not need full autonomous agent reasoning. They offer the largest libraries of pre-built integrations — often thousands of SaaS connectors — and require minimal technical expertise to configure.

General-Purpose Agent Platforms#

Platforms like Lindy AI and Relevance AI offer a broader set of agent capabilities, including multi-step reasoning, custom knowledge base integration via retrieval-augmented generation, and more complex agent workflows. These platforms target business teams and technical operators who need more than simple workflow automation but do not want to build custom agents from scratch.

For a direct comparison, see Lindy AI vs. CrewAI and the Lindy AI Review and Relevance AI Review.

Enterprise Vertical Agents#

Platforms like Moveworks focus on specific enterprise verticals — IT service management and HR in Moveworks' case — with agents pre-trained on domain-specific knowledge and pre-integrated with enterprise systems (ServiceNow, Jira, Workday). These platforms compete on depth of domain knowledge and enterprise system integration rather than general configurability.

Developer-Focused Managed Agent Infrastructure#

Some platforms target developers who want the operational benefits of managed infrastructure without sacrificing code-level control. These platforms typically provide SDKs and APIs rather than no-code interfaces, handling hosting, scaling, and monitoring while letting developers write custom agent logic. This category blurs the boundary between AaaS and Platform-as-a-Service (PaaS).

People collaborating at a conference table with pens and documents illustrating enterprise team workflows enabled by AaaS

Build vs. Buy: Decision Framework#

The central question for any organization evaluating AaaS is whether to buy a platform or build a custom agent. This is not a binary choice — many organizations use AaaS for some use cases while building custom agents for others.

Favor AaaS when:#

Speed matters: You need to deploy an agent in days or weeks, not months
Engineering capacity is limited: Your team lacks ML engineering experience
Use cases are standard: Your needs align well with what the platform offers
Data governance requirements are moderate: You can work within the platform's compliance posture
Iteration speed is critical: You want to change agent behavior quickly without redeployment cycles

Favor building when:#

Unique requirements: Your use case requires capabilities no platform offers
Deep integration needs: You need tight integration with proprietary internal systems
Cost at scale: Subscription fees become prohibitive at high usage volume
Data sovereignty: Regulatory requirements prevent sending data to third-party platforms
Full control: You need fine-grained control over model selection, prompting, and orchestration logic

Most teams should start with an AaaS platform to validate their use case before investing in a custom build. A pilot on a managed platform provides real user feedback at low cost and risk.

Pricing Models#

AaaS platforms use several pricing structures:

Per-seat subscription: Fixed monthly fee per user, common for internal-facing tools
Per-run pricing: Charged per agent execution or task completion, aligns costs with usage
Consumption-based: Charges based on underlying LLM tokens, API calls, or compute consumed
Tiered plans: Fixed tiers with capacity limits (number of agents, runs per month, integrations)
Enterprise contracts: Negotiated annual contracts with custom pricing for large deployments

Understanding the pricing model is critical when evaluating total cost of ownership. A per-run model that looks cheap in a pilot can become expensive at production scale if the agent runs thousands of tasks per day.

Service Level Agreements and Enterprise Considerations#

Enterprise AaaS deployments require more than a capable product. Key contractual and operational considerations include:

Uptime SLAs: What availability guarantees does the platform commit to, and what remedies exist for downtime?
Data processing agreements: How does the platform handle data residency, retention, and deletion? Is it GDPR, SOC 2, or HIPAA compliant where needed?
Model access and continuity: What happens if the platform changes its underlying LLM provider? Can you pin to specific model versions?
White-labeling and branding: Can you present the agent under your own brand to end users?
Integration access: Does the platform support the specific enterprise systems your agents need to connect to?
Support tiers: What level of technical support is available, and what are the response time commitments?

For teams evaluating enterprise options specifically, see Enterprise AI Agents.

Human-in-the-Loop in AaaS Platforms#

Most production AaaS deployments include Human-in-the-Loop checkpoints — moments where the agent pauses and routes a decision, draft, or action to a human for review before proceeding. AaaS platforms typically provide built-in interfaces for these handoffs, including approval queues, notification integrations, and audit trails.

This is particularly important for high-stakes tasks: sending external communications, modifying financial records, or taking actions with significant downstream consequences. The human-in-the-loop controls in AaaS platforms vary significantly in sophistication, and evaluating them should be part of any procurement process.

Frequently Asked Questions#