🤖 Building AI Agents for Enterprise: Inside Portia AI's $5M+ Funded SDK

In the rapidly evolving world of artificial intelligence, the promise of AI agents transforming enterprise workflows is undeniable. Yet, many companies find themselves stuck in the proof-of-concept (POC) phase, unable to transition AI agents into full production due to critical challenges around permissions, control, and reliability. On the AI Chopping Block podcast, I had an insightful conversation with Mounir Mouawad, CEO and co-founder of Portia AI, an open-source SDK designed to build production-ready AI agents with a special focus on regulated industries.

With a background spanning Amazon, Google, and Stripe, Mounir brings deep expertise in launching large-scale tech products and a unique vision for how AI agents can be safely and effectively integrated into enterprise workflows. Portia AI has already raised over $5 million in funding and is actively working with design partners to solve the notorious "permission death loop" problem that stalls 70% of AI agent projects.

In this comprehensive article, we'll explore the core challenges enterprises face with AI agents, how Portia AI's SDK addresses these issues, real-world applications, the economics of AI agents versus traditional SaaS, and thoughts on the future of work and AI talent in Europe. This piece is crafted to give you a deep understanding of the nuances behind AI agent adoption, inspired by the detailed discussion on the AI Chopping Block podcast.

What is Portia AI? A Developer-Centric AI Agent SDK

Portia AI is an open-source software development kit (SDK) that empowers developers to build AI agents ready for production environments. Unlike many AI tools that focus on general experimentation or low-code/no-code solutions, Portia AI emphasizes robustness, control, and transparency—key requirements for regulated industries such as fintech, healthcare, and ecommerce.

Mounir explains that their SDK is a horizontal platform, meaning it can be applied across diverse applications but is designed with features that resonate particularly well with enterprises handling sensitive data and strict compliance requirements. The SDK enables developers to embed AI agents into workflows that require deterministic control, human oversight, and secure authentication mechanisms.

One of the most significant distinctions of Portia AI is its focus on preemptive visibility and workflow consistency. Enterprises want to know in advance how a large language model (LLM) will respond to prompts and expect consistent, predictable behavior in key processes. Additionally, Portia AI offers real-time monitoring of agent activities, allowing humans to intervene or validate actions when necessary.

This level of control is essential in regulated environments, where mistakes can have costly or dangerous consequences. The SDK introduces the concept of execution hooks, points within a workflow where the AI agent pauses to request human input before proceeding. This feature ensures that critical decisions—such as issuing refunds or approving sensitive transactions—are never fully automated without oversight.

Handling Authentication and Permissions at Scale

A unique challenge in enterprise AI agent deployment is managing permissions securely. Portia AI tackles this by integrating human authentication directly into the agent's operation. For API-based tools, this means agents request OAuth tokens with scopes approved by human users at runtime. For browser-based interactions, control of the session temporarily switches to the human, who logs in directly. The session state, including cookies, is then passed back to the agent to continue its task autonomously.

This approach avoids the complex and error-prone practice of mirroring permissions across systems to control agents, which often leads to what Mounir calls the "permission death loop." This loop occurs when enterprises spend months building AI agents but hit roadblocks due to permission management challenges, ultimately stalling projects in the POC phase.

The Permission Death Loop: Why Most AI Agents Get Stuck in POC

One of the most eye-opening insights from the conversation was the exploration of the "permission death loop" that keeps roughly 70% of AI agent implementations from moving beyond proof of concept. Mounir shared that while many teams build prototypes successfully for small groups, scaling to full production presents a host of complications.

Enterprises need to guarantee that agents operate with explicit user consent, sending communications or performing actions strictly on behalf of authenticated humans. They also need to ensure traceability, associating each agent action with a specific end-user identity to maintain accountability.

Without these capabilities, enterprises risk regulatory non-compliance, data breaches, and loss of trust. Mounir emphasizes that to get AI agents into production, the end user must be treated as a first-class citizen within the system—meaning permissions, tokens, and workflow plans are all linked explicitly to a human operator.

Reliability and Scalability Concerns

Aside from permissions, reliability remains a significant challenge. Large language models are inherently stochastic, and ensuring consistent, accurate outputs requires ongoing validation and testing. Portia AI encourages enterprises to run continuous evaluation (EVA) tests to detect regressions caused by code changes or model updates.

Moreover, production-scale deployments introduce technical issues such as memory limits, context window flooding, latency, and cost inflation due to token usage. Handling large datasets or paginated API calls without degrading performance is an area where Portia AI aims to excel, helping teams optimize their workflows and resource consumption.

Why Focus on Regulated Industries?

Mounir's decision to target regulated industries like fintech, healthcare, and ecommerce is partly influenced by his extensive background at Stripe, Google Pay, and Amazon. These sectors have pressing needs for AI agents that can operate with tight controls, transparency, and compliance.

For example, in fintech, automating customer onboarding, KYC (Know Your Customer) checks, and billing reconciliation can save significant time and reduce errors. Healthcare applications might involve processing notes or scans to support preliminary diagnoses without replacing doctors but speeding up workflows.

These industries share a common requirement: the upside of automation is high, but the downside risk of errors is also significant. This makes human-in-the-loop mechanisms and deterministic controls not just preferable but essential.

Portia AI’s Go-To-Market Strategy: Balancing Open Source and Sales

Portia AI’s approach combines the strengths of open source with targeted sales efforts. By releasing an open-source SDK, they invite developers to contribute, customize, and extend the platform. This fosters a vibrant community that accelerates innovation and collective learning.

At the same time, Mounir and his team engage with design partners through POCs and pilot projects to refine the product and demonstrate real-world value. This dual strategy is somewhat new for the founders, who admit launching open-source products is a humbling and complex process.

Developer marketing plays a critical role here, as the company seeks contributors who provide feedback, file issues, and help shape the product roadmap. Engaging with the developer community through GitHub, Discord, and YouTube tutorials helps build trust and awareness.

Why Target Developers First?

Mounir explains that AI agent SDKs are deeply embedded in a company’s technology stack, and developers need control and flexibility. Open source meets this requirement, allowing engineers to customize the SDK to specific business needs.

Moreover, the AI agent space is nascent, with many players still experimenting. Open source encourages collaborative improvement and reduces the risk of vendor lock-in, making it attractive to technically savvy enterprises.

This strategy also aligns with the philosophy of "a rising tide lifts all boats." By contributing to and learning from the wider ecosystem, Portia AI strengthens its product while helping shape industry standards.

Competitive Landscape and Differentiators

While acknowledging strong competitors like CrewAI, whose founder is a respected voice in the AI agent space, Mounir stresses that the market is large, early-stage, and evolving rapidly. Portia AI chooses to focus on user problems rather than obsess over competitors.

He notes the unfortunate presence of less credible actors offering "snake oil" solutions, which can create confusion. Portia AI’s commitment to transparency and user-centric design helps differentiate the product as a reliable, production-ready SDK.

The key differentiators include:

• Human-in-the-loop execution hooks for deterministic control
• Robust permission and authentication management
• Real-time visibility and intervention during workflows
• Open-source flexibility for deep integration and customization
• Focus on regulated industries with high compliance requirements

What Can AI Agents Actually Do Today?

There's a lot of hype around AI agents automating entire companies or replacing jobs wholesale. Mounir offers a balanced, cautious perspective grounded in history and current technology trends.

He agrees with Sam Altman’s optimistic view that LLMs will continue to improve in reliability and specialization—some excelling at reasoning, others at audio, video, or tool usage. The cost of inference is dropping, and innovations in data center architectures promise more efficient, sustainable AI.

However, Mounir stresses that while some jobs will be impacted, others will evolve or be created. He draws parallels to the Industrial Revolution, ERP adoption, and mobile app development, where roles shifted rather than disappeared entirely.

For example, the rise of mobile apps created new jobs for device-specific testing. Similarly, the growth of AI will likely increase demand for human quality checkers, annotators, and reviewers to ensure AI responses remain accurate and safe.

Concrete Examples of Changing Roles

Mounir shares a recent conversation with a University College London undergrad who finds that interviews now emphasize product engineering and architectural thinking more than pure coding skills. This shift reflects the growing importance of designing systems that support complex AI integrations.

On the other hand, some manual roles are disappearing, such as data entry or repetitive processing jobs, especially in sectors like healthcare where AI-powered automation can reduce the need for human labor.

While the transition creates uncertainty, Mounir remains cautiously optimistic that the AI revolution will produce both winners and losers. The best approach is to ask the right questions and prepare for a range of outcomes.

Real-World Use Cases and Cost Insights

Portia AI’s design partners are already using the SDK to automate tasks like onboarding, KYC, and email-based data collection. These AI agents can recursively interact with multiple people, gather documents, cross-check information against public registries, and reduce onboarding queues by up to 50% with minimal risk.

A particularly compelling example is an agent that automates invoice collection across multiple billing portals and email inboxes. This agent retrieves invoices within specified date ranges and uploads them into accounting systems, saving hours of manual bookkeeping each month.

Mounir also shares a personal anecdote: he created an agent that analyzed a list of about 30 event attendees to identify first or second-degree connections and educational backgrounds, such as whether they attended London Business School. This task, which would have taken hours manually, was completed by the agent in about five minutes at a negligible cost.

These examples illustrate how AI agents built with Portia AI can reduce reliance on expensive SaaS subscriptions charged per user seat and instead offer flexible, cost-effective automation tailored to specific business needs.

Pricing Dynamics in the AI Agent Market

When asked about the high prices charged by some AI agent providers—such as Twitter automation bots reportedly charging $20+ per hour—Mounir explains that many companies are still in an "El Dorado" phase, capitalizing on demand before market competition drives prices down.

He compares this to the early days of SaaS and expense management, where initial pricing was high until supply and demand balanced out. As more companies offer agent capabilities, the differentiating value decreases, leading to more competitive pricing.

Portia AI’s pricing model, based on executions rather than seats, aims to provide reasonable, scalable costs aligned with actual usage, helping enterprises adopt AI agents without prohibitive expenses.

The AI Talent War in Europe and the UK

The conversation also touched on the intense competition for AI talent, especially with major U.S. companies like OpenAI expanding their presence in Europe and paying premium salaries.

Mounir acknowledges the challenge but points out that the startup experience offers unique advantages, such as faster decision-making, closer collaboration with founders, and broader responsibilities. These factors attract talent that values impact and equity upside over large-company stability.

Europe and the UK continue to be hotbeds of AI innovation, with DeepMind, Mistral, and other leading companies based there. The region offers outstanding talent, a growing ecosystem, and substantial opportunities for startups like Portia AI to thrive.

While some founders consider relocating to the U.S. for scale, Portia AI aims to maintain a strong presence in Europe, supported by successful events like their April hackathon showcasing exceptional developer contributions.

Building the Team and Company Culture at Portia AI

Looking ahead, Portia AI seeks to attract versatile, product-minded engineers who thrive in a collaborative environment. The team values debate, diverse perspectives, and contributions that span architecture, business strategy, and user experience.

Engineers at Portia AI are expected to think deeply about the end user and developer experience, as well as the business models their work enables. Fire in the belly and a bias toward action are also essential traits.

For non-engineering roles, adaptability, willingness to learn, and versatility are crucial, given the fast-shifting nature of the AI space.

Conclusion: Embracing the Future with Portia AI and the AI Chopping Block

The journey toward production-ready AI agents is fraught with challenges, from permission management and authentication to reliability and cost control. Portia AI stands out by addressing these issues head-on with an open-source, developer-friendly SDK that prioritizes transparency, human oversight, and security.

As enterprises increasingly seek to harness AI agents for complex, regulated workflows, tools like Portia AI will be critical in bridging the gap between proof-of-concept experiments and scalable, trusted production systems.

Moreover, the evolving landscape of AI talent and the future of work will demand adaptability, new skills, and thoughtful integration of human and machine capabilities. Portia AI’s approach exemplifies how thoughtful design can unlock AI’s potential while respecting the nuances of enterprise needs.

For developers, startups, and enterprises eager to explore AI agents with confidence, Portia AI offers a promising path forward. I encourage you to check out their open-source SDK, join their community, and experiment with building AI agents that are not just intelligent but also responsible and reliable.

Stay tuned to the AI Chopping Block for more in-depth conversations with AI founders and entrepreneurs shaping the future of work and technology.