TL;DR

• Built the execution layer required for agentic systems to operate reliably across real software environments, with a focus on orchestration, integration, and governance.
• Progressed from foundational API and action discovery to multi-step workflows and coordinated execution across applications, services, and data sources.
• Designed agent experiences to operate where work happens, including embedded interfaces, search, APIs, MCP servers, and orchestration layers.
• Delivered a builder surface that supports no-code, low-code, and engineering teams, balancing speed, extensibility, and control.
• Launched open-source infrastructure to address a market-wide gap, providing shared building blocks for production-grade agent development.
• Enabled customers to reduce time to value while scaling agentic workflows through reliable execution and integration depth.
• Continued forward with a focus on partnership, execution quality, and systems that evolve alongside customer needs.

‍

What We Built for the Agentic Era

A Year of Execution, Integration, and Real Customer Work

The agentic era did not begin with interfaces. It began with a hard realization inside real software environments: intelligence without execution creates curiosity, not outcomes.

Over the past year, teams across industries experimented with agents that could reason, summarize, and converse. Many of those experiments stalled when agents encountered the realities of production software. APIs varied widely. Business logic lived across systems. Failures were opaque. Trust eroded quickly.

Adopt AI spent the past year building for that reality. The work focused on execution, orchestration, and reliability across real applications, informed by continuous engagement with customers operating complex systems. The result was not a single feature or launch moment, but a platform shaped by how agents actually need to function in production.

From Intelligence to Execution

Early customer conversations revealed a consistent gap. Agents could interpret intent, but struggled to act.

Operational software exposed capabilities through fragmented REST and GraphQL APIs, undocumented workflows, and assumptions embedded deep in user interfaces. Agent frameworks handled reasoning well, yet lacked the structure required to invoke actions safely, sequence multi-step workflows, or validate outcomes.

Execution failures became the dominant constraint.

Platform development focused on execution primitives rather than surface-level interaction. Engineering investment centered on API discovery, schema extraction, and action abstraction. Product work emphasized how real applications behaved under load, across environments, and across user roles.

Integrations emerged as the central challenge and the primary opportunity. Agents confined to a single system plateaued quickly. Agents capable of coordinating across systems amplify impact and efficiency far beyond what siloed tools can do.

Orchestration as the Missing Layer

Customer use cases expanded beyond isolated tasks into end-to-end workflows.

Early adopters in insurance, fintech, and enterprise software consistently pushed beyond point solutions, prioritizing cross-system workflows that reflected how work actually moves across organizations. Insurance teams required coordination across policy administration, claims, and review systems. SaaS teams explored workflows spanning CRM, billing, analytics, and support. Operations-heavy environments demanded agents that could connect planning tools, inventory systems, and financial workflows.

Meeting those needs required a shift from single-action execution to orchestration.

Platform development accelerated around several core capabilities:

• Zero-shot API discovery across REST and GraphQL systems, forming the foundation of ZAPI
• Canonicalization of disparate APIs into consistent, executable action models
• Workflow orchestration that allowed agents to chain actions across systems
• Structured inputs and outputs that supported validation, auditing, and recovery

Several of these concepts, particularly around zero-shot API discovery and action generation, were formalized through patents to protect the underlying approach. The intent was not defensive posturing, but recognition that reliable orchestration required novel infrastructure rather than incremental layering on existing tools.

The resulting architecture enabled higher-order agent behavior. Agents interpreted intent, selected appropriate actions, sequenced execution across systems, and verified outcomes as workflows progressed. Execution quality determined adoption, and agents that failed silently or unpredictably never progressed beyond pilot use.

Products Shaped by Real Work

As patterns stabilized, the platform evolved into defined products shaped directly by customer workflows.

The Agent Experience: Where Work Happens

End users needed agents available where work already occurs, not confined to a single interface or application surface. Product teams prioritized flexibility, clarity, and trust over forcing a single interaction model.

Agent experiences were designed to operate across multiple environments depending on customer needs and system architecture. In some cases, agents lived directly inside applications. In others, agents operated through search bars, web interfaces, APIs, or MCP servers that coordinated execution across systems.

Flexibility mattered because workflows rarely live in one place. Teams move between tools, data sources, and operational surfaces throughout the day. Agent adoption increased when interaction models aligned with those realities rather than forcing new behavior.

Capabilities focused on:

• Unified search and embedded interfaces within applications
• API-based agent delivery for teams building custom experiences
• Multimodal interaction, including voice, where workflows require it
• Session-aware agents capable of handling multi-step workflows
• Operational actions such as bulk upload, download, and system updates
• Transparent reasoning and output visibility to support validation

Successful agent experiences met users where they worked, surfaced the right actions at the right moment, and completed tasks without introducing friction or uncertainty.

(Learn More)

The Agent Builder: Control Across Technical and No-Code Surfaces

Agent creation required a surface that supported both speed and precision. Adoption depended on enabling product managers, operations teams, and no-code builders to define agent behavior confidently, while still giving engineering teams the control required to govern execution at scale.

The Agent Builder was designed as a shared control plane for agent behavior, accessible across technical and no-code workflows. Builders could define actions, workflows, and constraints once, then deploy them consistently across multiple execution surfaces based on customer and system needs.

Capabilities focused on:

• Natural language action creation with structured validation that supports no-code and low-code agent building
• Explicit multi-step workflow definition with controllable execution logic
• Sandboxed environments for testing actions before exposure to users or systems
• Support for bring-your-own agents, prompts, and models to preserve advanced engineering flexibility

Accessibility and governance advanced together. Non-technical teams gained the ability to create and iterate on agent workflows without introducing risk, while engineering teams retained visibility, auditability, and control over execution. Agent building scaled when ownership extended beyond a single team without sacrificing reliability.

(Learn More)

Launching Open Source to Democratize Agent Building. Make everyone a builder.

As agentic AI moved from experimentation toward production, a shared market gap became clear. Teams could build prototypes quickly, but production readiness stalled without standard infrastructure for execution, orchestration, and evaluation.

Adopt AI launched the Open-Source Agent Stack to address that gap directly. The stack provides a practical foundation for building production-grade agents without requiring teams to reverse-engineer APIs, rebuild orchestration frameworks, or maintain brittle one-off tooling.

The open-source release delivers core building blocks, including zero-shot API discovery, agent orchestration, integration bridges to existing frameworks, testing interfaces, and automated evaluation workflows. Each component can be adopted independently or combined, allowing teams to start where they are without replacing existing systems.

Making these components open source reflects a broader commitment to the market. Agentic systems scale when shared infrastructure exists. Open tooling reduces friction, accelerates time to value, and allows teams to focus on delivering outcomes rather than rebuilding foundations.

(Read more)

Agents in Active Production Use

By mid-2025, customer conversations shifted from feasibility to expansion.

Agents were actively used to:

• Automate onboarding and setup workflows
• Resolve operational tasks without human intervention
• Coordinate actions across CRM, finance, and support systems
• Reduce time spent on repetitive, high-volume processes
• Deliver embedded assistance that completed tasks rather than deflecting them

Integrations formed the backbone of progress. Cross-system execution transformed isolated agent interactions into end-to-end workflows.

Deployment flexibility mattered equally. Some teams embedded agents directly into products. Others exposed agent capabilities through APIs or system integrations. Platform adoption increased when teams could choose deployment models aligned with their architecture.

Launching With Operational Context

We launched in summer 2025 after months of active customer use and iteration, bringing products to market already shaped by real production constraints.

By launch, the platform supported:

• Multi-step workflow execution across systems
• Enterprise-grade reliability and observability
• Flexible deployment through embedded interfaces, APIs, and SDKs
• Governance mechanisms required by regulated industries

As a result, customer evaluations emphasized coverage, extensibility, and reliability over novelty. Teams expanded agent usage only after workflows consistently proved dependable across environments.

Enterprise Execution Becomes Explicit

The second half of the year focused on strengthening the execution layer required to operate agentic systems at scale.

Product capabilities expanded to support:

• Multi-environment and multi-product execution
• Advanced action builders for natural language workflow creation
• Broader API discovery, including GraphQL
• Prompt and configuration vaults for customer-owned assets
• Evaluation frameworks to test agent behavior against expected outcomes
• On-prem and customer-controlled deployment options

Execution quality became a primary concern. Latency improvements, trigger-based evaluations, and encryption-aware payload handling addressed real integration failures encountered in production environments.

Interoperability advanced through Standard and Custom MCP catalogues, enabling agents to operate across third-party systems without custom plumbing. Data was elevated to a first-class tool, allowing agents to reason over integrated sources as part of execution. Smarter execution flows emerged as agents coordinated actions, validated outcomes, and adapted behavior across complex workflows.

What Customers Actually Gained

A credible year in review cannot stop at feature delivery. Real value only exists when capabilities change how teams work and what they can deliver.

Several outcomes consistently defined what customers gained.

1. Faster Agentification With Real Impact

Automatic API discovery and action generation shortened the path from idea to execution. Teams spent less time configuring infrastructure and more time applying agents to real workflows.

Example: A SaaS team enabled an agent to provision accounts across identity, billing, and admin systems using automatically generated actions that were refined over time as requirements evolved.

2. Choice Without Compromise

Adoption accelerated when teams no longer had to choose between internal innovation and shared infrastructure. Agentic systems worked best when organizations could build differentiated logic where it mattered while relying on a common execution layer for scale and reliability.

Support for bring-your-own agents, prompts, and models preserved flexibility without sacrificing orchestration, observability, or governance.

Example: An enterprise platform team connected an internally built agent to shared execution and observability workflows, enabling broader adoption without reworking existing systems.

3. Reliability as a First-Class Capability

Trust emerged when agent behavior became predictable and observable. Guardrails, evaluations, and structured validation moved reliability into the core of agent design rather than treating it as an afterthought.

Example: An operations team automated a reconciliation workflow across finance systems, using validation and observability to surface exceptions early and expand automation confidently.

4. Multi-App Execution as the Foundation for Agentic Systems

Meaningful value emerged when agents operated across systems rather than within isolated tools. Integration depth and orchestration enabled agents to coordinate actions, validate outcomes, and handle real workflows end to end.

Example: A services organization deployed an agent that coordinated CRM, ticketing, document storage, and approvals to resolve customer requests with minimal manual intervention.

The Direction Forward

The agentic market is entering a more demanding phase, and Adopt AI is evolving alongside customer expectations. Early experimentation is giving way to requirements around reliability, integration depth, and measurable outcomes.

Customers are no longer evaluating agents as assistants. Expectations center on systems that can take responsibility for real workflows, operate across applications, and deliver value quickly without introducing operational risk. Execution quality, time to impact, and the ability to evolve with customer needs increasingly define success.

Adopt AI’s direction forward builds on the foundations established over the past year. Continued investment in orchestration, integration, and governed execution reflects a commitment to agents that function inside real systems rather than alongside them. Progress continues through close partnership, rapid iteration, and shared learning grounded in outcomes.

The next phase focuses on extending those capabilities further. Deeper orchestration, broader integration surfaces, and tighter alignment between agent behavior and business-critical workflows remain central to how Adopt AI helps teams reduce time to value while increasing confidence at scale.

Agentic systems that earn trust through execution will shape how software evolves. Building toward that standard in partnership with customers defines Adopt AI’s path forward.

‍