Are you leveraging APIs to 

• Provide client services?
• Build partner ecosystems?
• Power AI agents?

If not, it's time to start.

The era of agentic AI is here. As these autonomous agents rely on APIs, we are seeing a surge in API usage and a new wave of API-driven partner ecosystems.    

This means now's your chance to deliver more value to your customers and grow API revenue-but it also means your APIs may quickly become obsolete. 
​
Here's how to sell API services in the agentic AI era—​and how to prepare for what's next. 

1000x API Growth

As mentioned in my recent blog post on agentic AI, client agents act on behalf of a human user. They contain API calls to other services such as "make a payment" or "get invoice status." 

(Increasingly these API services are contained inside service agents powered by MCP servers. Anthropic's Model Context Protocol (MCP) specifies the structure for how agents call one another to request services. When agents are built using an agentic framework, such as LangGraph or CrewAI; the framework enables agents to call these MCP servers.)

The more agents we build, the more API services they'll consume—​whether directly or  through MCP servers. This is why the API services provided by software vendors are even more important in the era of agentic AI.

As Aaron Levie CEO of Box said, "the de facto model of software integrations in AI is one primary AI Agent interacting with the APIs of another system. This is a great model, and we will see 1,000X growth of API usage like this in the future."  ​

Using Agents to Sell APIs

Companies can also use LLMs to make their APIs easier for developers to use  - whether they are building agents or traditional applications. As is becoming increasingly true for support across a variety of industries, software companies who are providing APIs to their partners can create an AI support agent. LLMs power these agents which answer developer questions and provide real-time support.   Companies such as VoPay have created developer support agents by pre-populating an instance of ChatGPT with FAQs, code samples, and other documentation.

Of course, it doesn't stop there. Software companies can use LLMs to help generate code snippets or even most of a working application tailored to the developer's needs. Stripe has an AI Dev assistant and Google Cloud has Gemini Code Assist which integrates into IDEs and provides not only chat-based assistance for developers using their APIs but also contextual code completion and even code generation.​

​
UI-Driven Agents 
​
Today agents will be easier to build and more reliable if they engage with services that provide a explicit contract about how they behave, an API.  However some agents are being designed to watch users work and mimic their actions. This is a very powerful paradigm as it will augment human work by doing many of the repetitive tasks we do daily. Vendors providing "agentic robotic process automation (RPA)" or "LLM-enhanced process agents" include RPA companies such as UiPath and Automation Anywhere. 

These agents aren't using APIs because they are acting like a human user and interacting with a software company's traditional application graphical user interfaces (UI).  UIs change and their usage isn't always clear (even to us humans), but as agents evolve they will be more capable of navigating a traditional software UI. 

Since these type of agents don't use APIs, so this is one way that agents may render your APIs obsolete. 
​

From API Calls to Conversations

However the most effective and impactful agents will continue to use APIs. Although as vendors' services start becoming more agentic; traditional JSON formatted APIs may give way to more conversational or agent-native interfaces. 

Also, as mentioned in my earlier blog, the revolution will take another leap when Agent-to-Agent communication becomes more prevalent.  This will fuel a further shift towards conversational interfaces. 

Google's new Agent2Agent protocol prescribes agent-to-agent communication (A2A) to use JSON-formatted messages.  These messages can trigger underlying services that are either traditional JSON-based APIs or natural language-based interfaces. 

As the providers and the consumers of APIs become increasingly agentic, these powerful programs will increasingly be using their natural language interfaces for their interactions. This is especially true with A2A communication which enables more sophisticated and powerful capabilities.

=> Will agents and agent-to-agent communication make today's application APIs obsolete?

=> How are you evolving your API services for the agentic AI era? 

(Views are my own and do not represent those of any current or former employer.)​

More enterprise software applications will shift from a per-user to a per-transaction pricing model. Pricing will be based on transactions or tasks completed such as APIs called, expense reports processed, or marketing emails sent because one agent can do the work of many human users. With the advent of agents, the business of software may change even further as companies "hire agents" much like they hire human workers to generate specific business value. Agents could deliver outcomes such as leads from marketing emails or savings from automating customer onboarding. (For more see my previous blog on SaaS pricing.)

Previously a software vendor was limited by the number of seats or end users it could enable. However, as Aaron Levie CEO of Box said, “AI Agents provide another vector of growth for software by enabling companies to essentially buy 'work' from their software vendor. And because this work is elastic, enterprises can throw AI Agents at both large and small problems in the business alike — like, reviewing a batch of contracts, qualifying a lead, transcribing a doctor visit, writing lines of code."  Or as Yoko Li from A16Z succinctly said "The market is growing so much bigger because there are use cases we haven't even thought about yet." 

​
🛠️ Design Shift: Graphical -> Conversational UX

Users won't click ten screens; they'll just say,  "pay all invoices that are due tomorrow", with a possible caveat "but confirm with me first." These type of agents, which act on behalf of a human user, are called client agents. Designers and developers will shift focus from workflow optimization—which agents will handle—to defining agent prompts, rules, and data structures that encapsulate industry knowledge and deliver distinct value.

In many applications the UX will be a mix of traditional graphical UI and conversational agents. This was my experience when building a simple agentic AI app using Replit.

More interesting, however, is when an agent is designed to use multiple systems - eliminating the need for humans to navigate between separate applications. For example a user can request an agent to "book a flight to get me to the conference I am going to next week." The agent would interact with a calendar app, a travel booking site, and a payment system. If this type of use case requires calling and coordinating responses between multiple agents, such as date and time availability,  this agent would be considered an orchestration agent. ​

🤝 AN EVEN BIGGER SHIFT: AGENT-TO-AGENT COMMUNICATION 

Today, agents respond to human prompts. Tomorrow, the “user” could be another agent.   The Wall Street Journal (Steve Rosenbush 3May2025) says that companies should start planning for this next stage of AI, the orchestration of multiple agents across their business. It reported that Accenture has more than 50 multiagent systems today for a range of industries and markets. Aaron Levie says agent-to-agent communication will be the biggest unlock of AI. 
​
Orchestration agents typically operate within a predefined workflow involving various other agents.  What if agent interactions are not constrained by a specific workflow? Agent-to-agent communication will enable software to automate and solve problems in novel ways that we have yet to consider.

When the World Wide Web first debuted, it was static or read-only. Most people thought what we now call Web 1.0 could possibly replace magazines or libraries. At the time few people considered its evolution to Web 2.0 or the read-write web which delivers dynamic content, social media, and business applications. Even fewer could have imagined a semantic and de-centralized Web 3.0.

As we evolve from predictive AI to also using generative AI, we are still optimizing the ways we solve problems. This is even more true with agentic AI especially as it starts including agent-to-agent communication. 
 
=>What are new approaches we aren't yet considering with this powerful new paradigm?

What business problems may be solved more effectively but in a manner unfamiliar to humans when we start building agents which aren't design to act on behalf of a human but to communicate and coordinate with other agents?  Real-time logistics involving hundreds of suppliers and dozens of currencies during a global crisis? Corporate strategies? Peace treaties?  Building more powerful agents? 

Overview of SaaS Partnerships
​from when I worked at enterprise Software as a Service companies


Technology Providers: Supply capabilities used in SaaSCo's offering
Provide services required for a specific market or product

• Database technology providers
• Hosting / cloud providers
• Information / data providers

Owned by Technology and Operations organizations
Non exclusive relationships


Service Providers: Implementation, support, etc. for a specific market  

• System Integrators (SI) for a specific region or for a specific software package
• Support services for a given market (such as a language / country where SaaSCo's in-house support not available)
• MSP - managed service providers (overlap with technology providers as could be outsourced IT)

Owned by the department at SaaSCo that usually provides this service such as implementation or support team

Relying on expertise in market or product type so exclusivity unlikely unless referral or resell involved 


Go-to-Market Referral or Distribution Resell Channel Partners:
Assist in selling SaaSCo's offering in given market (region or segment) or channel

• Affiliate marketing
• Referral partner
• (Value Added) Resellers
• Online marketplaces

Owned by Marketing and Sales teams; and specifically Channel Sales & Marketing

Most likely involves exclusivity for SaaSCo’s market


Product Partners (ISV - Independent Software Vendor)
Enhance client offering 

• Provides key capability or offering that is needed to help sell SaaSCo’s offering for given market or segment
• Helps create an ecosystem which is a broader value proposition to the client​

For one SaaS company approximately 3/4 of clients said presence of a partner ecosystem and the promise that a wider base of solutions than just SaaSCo's solution was a principle reason they picked the (higher priced) SaaSCo offering.  According to CrossBeam, one company had 34% higher annual contract value (ACV) on deals done with product partners.

Owned by Product organization
Exclusivity in resell model but less likely in referral model


Although!  Often there are overlaps across these categories

• System Integrators (especially Global System Integrator GSIs) can also refer or resell products which they integrate. 
• Product partners can also be referral partners encouraging clients who do not already have SaaSCo’s product to use it as it makes their offering more compelling and valuable. In this case the channel sales team will own or at least dictate the terms of the referral agreement with the Product Partner, even if the product partnership team is the lead.
• Distribution partners can be white label partners integrating SaaSCo's solution with their own offering; but partnership must be enabled in conjunction with product teams market offering strategy.

Two Distinct Product Partnership Business Models for SaaS Companies

There is a big gap between these two product partnership models because in the resell model SaaSCo owns the revenue and the risk! 

Referral
Enables solutions and sales for SaaSCo especially with regional / vertical opportunities; drives some revenue

• Percentage (with minimums) of what partner charges for a “connector” to the SaaS company’s services or data
• and / or a portion (maybe roughly 1/4) of the total offering that the partner charges - depending on how much SaaSCo needs the client's solution to more effectively sell their own (for example a partner's compliance offering that is required to sell SaaSCo's offering in a certain market). 
• Also a one-time referral fee for a partner offering purchased by SaaSCo clients.

Access to basic marketing mostly focused around SaaSCo’s marketplace or “App Store” listing

Limited sales support into SaaSCo sales organization and client base

Minimum support other than integration

No/minimal influence on partner's product development roadmap

Premium Referral program could involve additional fees from partner for specific additional marketing and sales opportunities

Minimum risk vetting since not on SaaSCo paper (not a SaaS company contract); although realizing SaaSCo brand still takes some risk if there is referral


Resell
New product offering for SaaSCo; driving significant revenue

• Negotiated percentage of partner core offering list price (very roughly in 50% range although this could vary widely depending on which party is responsible for which activities - such as support; and which party more needs the other party - who has the power!)
• Aspects of deal may include certain exclusivity (regions, accounts), sales channel conflict avoidance strategies such as double commission, list price discounts, and SaaSCo committed volumes.

Negotiated co-marketing; and SaaSCo marketing like other SaaSCo’s products.

Sold like other SaaSCo product with customer / market restrictions and minimum volumes negotiated as part of deal.

Support negotiated but generally Level 1 and Level 2 support by SaaSCo and detailed Level 3 support by partner.

Influence on partner's product development roadmap potentially negotiated as part of deal.

Premium resell may be white labelled where SaaSCo has exclusivity across larger markets and owns more of partner roadmap.

Since on SaaSCo paper, need to do full risk review and create processes across entire operations between partner and SaaSCo

As a reminder! 
Partners want access to data and functionality and access to brand, marketing, sales teams, and clients.

  
Recently I heard Marty Cagan speak as part of the McKinsey Product Academy Series. In the past Marty has spoken at my class and I have reviewed his book Inspired, my favorite book on product management.
 
Therefore I thought it was finally time for me to read the book he wrote with Chris Jones in 2021, Empowered. It can easily be used as a reference book when considering any one of a range of topics about how companies create extraordinary software products. However there are a few central themes which I thought would be helpful to describe here.
 
Two of the main messages around creating empowered product teams are:

• Give product teams problems to solve, rather than features to build. Empower them to solve those problems in the best way they see fit.

• Leadership is about recognizing that there's a greatness in everyone, and your job is to create an environment where that greatness can emerge. - Bill Campbell

 
Feature vs Empowered Teams (solve customer problems, achieve outcomes):
     A central theme in the book is the distinction between feature product teams and empowered product teams. A feature team implements features and projects (output), and as such isn’t empowered or held accountable to results. In contrast, an empowered team is accountable for solving a customer problem or achieving a specific outcome, and is empowered to do so in the best way they see fit.   

Difference Between Strongest Product Companies and the Rest 

1. Role of Technology (it is the business, not an expense):
     So many companies still have the old IT mindset when it comes to technology. It's viewed as a necessary cost rather than the core business enabler it needs to be. The people who work on the technology teams are literally there “to serve the business." In these scenarios, the product and technology teams are disconnected from the real customers—in fact, they're encouraged to think of their stakeholders as their customers. For strong product companies, technology is not an expense, it is the business.
 
2. Role of Product Leaders (coaching, strategy, managing to results, deep business and product knowledge):
     Most product leaders are responsible for staffing the in-house feature factory, and keeping the trains running on time. The best product leaders are staffing and coaching product teams, creating product strategy, putting strategy into action, and managing to results

• You need to be very specific when identifying the most important business problems a product team should solve. 
• Your role as a leader is in helping everyone on the team achieve the competence necessary to solve those problems.

       Product Leaders need to have a deep business and product knowledge (and frequent customer interactions)

• Data (how the product is actually used)
• Industry and domain knowledge
• Business and company knowledge
• Product operational knowledge (how the product actually works)
• (most importantly) Users & customer knowledge

     Cagan recommends a minimum of three, one‐hour customer interactions each week. During weekly one-on-ones he likes asking about these customer interactions to see what the product person has learned. He also encourages the product person to share stories of what they experienced during these visits, and then to share these stories widely around the company. One reason for this is for the product person to establish their reputation as someone who has a deep and personal knowledge of the company's users and customers.

3. The purpose of product teams (product managers, product designers, and engineers)
     Consistent with the central theme of the book, Cagan believes that in strong product companies teams are given problems to solve, rather than features to build, and most importantly they are empowered to solve those problems in the best way they see fit and are then held accountable to the results.

​Importance of Product Vision, Strategy, & Discovery
In empowered product teams:

• Product vision describes the future we are trying that improves the lives of our customers 
• Product strategy helps us decide what problems to solve
• Product discovery helps us figure out the tactics that can actually solve the problems

Product vision keeps us focused on the customer and serves as a North Star 
     It provides the product organization with a common understanding of what we are hoping to accomplish together. A good product vision inspires ordinary people to create extraordinary products. A good product vision provides us with meaningful work.
     When a company has grown to the point where there are multiple product teams—supporting many customers with their constant needs. It is very easy for each product team to get caught up in their own problems and their own work, losing sight of the overarching goal. The product vision represents the common goal and constantly reminds us of the larger purpose.
 
Product Strategy: Based on insights, it is how we make the product vision a reality while meeting the needs of the company as we go
     While product strategy starts with focus, it then depends on insights.  Insights come only after you spend hours studying your data, your customers, the enabling technologies, and your industry. Insights can come from anyone or anywhere – salesperson, new technology, random comment from a customer, or an academic paper. They might pertain to the dynamics of our business, our capabilities, new enabling technologies, the competitive landscape, how the market is evolving, or our customers. The foundation for significant insights is the strategic context found in the company objectives, the company scorecard/dashboard, and the product vision. Insights are both quantitative and qualitative insights; and they need to be shared and communicated.
     Many companies have a stakeholder‐driven roadmap process, where they basically are trying to find a way to “fairly” divide up the engineering capacity across the different business stakeholders.  But by not picking your battles and focusing on the few truly critical problems, most of the product team's work  does not make an impact.
 
Product Discovery finds product market fit by considering value, usability, feasibility, viability (as discussed in Inspired) 

• Value – will users buy and use it (Product Mgr)
• Usability – can they figure out how to use it (UX)
• Feasibility – can we build it (tech arch)
• Viability – part of our overall business 

(Product Market Fit is the essential component for any successful product.)
     Discovery can’t be done by stakeholders, customers and prospects; they don’t know what is possible and they don’t know what solutions work
     Often customers try to discuss solutions but they really are discussing problems. It is natural for a prospect to try to dictate requirements for features, but a product manager's job is to work to understand their underlying issues and constraints, and then work collaboratively with your prospective customers to determine if there's a general solution that will meet their needs. This form of collaboration is at the heart of the customer discovery program technique mentioned above and discussed in Inspired.
 
​

Or John Doesn’t Know S**t about Crypto:
a mostly accurate overview with only a few misleading generalizations
​
It was great presenting to the Notre Dame Tech Forum on a few central crypto concepts:

1. What is money?
2. So why was Bitcoin invented?  
3. Wait, isn’t this all just a scam?
4. Blockchains and Distributed Apps –  powering the future !?
5. Distributed Finance – get a loan without talking to a bank
6. Enterprise Blockchains – so companies / problems I’ve heard use this?
7. Distributed Autonomous Organizations – the future of human society !?

​
​Below is the presentation and here is the recording of the event along with the presentation.
​

My favorite overview of DeFi that I have read so far is Decentralized Finance: On Blockchain- and Smart Contract-Based Financial Markets written by Fabian Schar and published by the St. Louis Fed. (Although a few things seem a bit out-of-date since it is a year old and Whiteboard Crypto on YouTube is a tad bit more accessible.) 

Here are a few of my takeaways from reading this article.

DeFi replicates existing financial services in a more open and transparent way. In particular, DeFi does not rely on intermediaries and centralized institutions. Instead, it is a blockchain-based financial infrastructure with open protocols and decentralized applications (DApps). Besides a rich instruction set DApps or smart contracts can also acts as custodian for crypto assets.
 
(DApps or smart contract theoretically could be any arbitrary computer program since they run on Turing complete blockchains (all but Bitcoin); although they are very inefficient. The ability to run more complex smart contracts is one of the biggest differences between Bitcoin and Ethereum block chains.  Blockchains like Solana are designed to address the inefficiency or scalability problems of blockchains like Ethereum).   
 
Native protocol crypto assets (BTC, ETH or cryptocurrencies ) are used to operate the blockchain. 
 
Crypto assets that are tokens are units of value that blockchain based organizations or projects develop on top of existing block chain networks. Token standards include ERC-20 for tokens which can interoperate with Ethereum's ecosystem of decentralized apps. ERC-720 are non-fungible tokens (NFTs) used to tokenize ownership of any arbitrary data. For example they can be the digital representation of a physical object such as a piece of art.
 
This means that tokens can serve a variety of purposes: including governance tokens for decentralized autonomous organizations (DAO), tokens that allow the holder to perform specific actions in a smart contract, tokens that resemble shares or bonds, and even synthetic tokens that can track the price of any real-world asset.

As you can see above, besides settlement layer (blockchain native protocols) and asset layer (including tokens) there is a protocol layer containing decentralized exchanges used by other DApps, an application layer, and aggregation layer to connect several applications and protocols

In decentralized exchanges, users do not need to deposit their funds which is the case with centralized exchange. Instead, users maintain control of their assets until the trade is executed. Trade execution happens atomically through a smart contract, meaning that both sides of the trade are performed in one indivisible transaction, mitigating the counterparty credit risk. Depending on the exact implementation, the smart contract may assume additional roles, effectively making many intermediaries such as escrow services and central counterparty clearing houses (CCPs) obsolete.
           
​Decentralized lending platforms and loans are an essential part of the DeFi ecosystem. Defi loans do not need to rely on trusted relationships via one of two methods. One method is credit being provided under the condition that the loan must be repaid atomically, meaning that the borrower receives the funds, uses, and repays them—all within the same blockchain transaction. A second way is loans can be fully secured with collateral. The collateral is locked in a smart contract and only released once the debt is repaid.

One type of collateralized loan platforms allow the user to create collateralized debt positions; in other words the user gets new tokens back by collateral locked in smart contract (or in the example below, just USD). For example MakerDAO is a decentralized protocol that is used to issue the USD-pegged Dai stablecoin.  First, the user deposits ETH in a smart contract classified as a collateralized debt position (CDP) (or vault). Subsequently, they call a contract function to create and withdraw a certain number of Dai and thereby lock the collateral. 

There are also collateralized debt markets. Instead of creating new tokens, it is also possible to borrow existing cryptoassets from someone else - either pooled (subject to supply and demand) or P2P (person to person).

Decentralized derivatives are tokens that derive their value from an underlying asset's performance (tokenized versions of stocks, precious metals, alternative crypto assets), the outcome of an event, or the development of any other observable variable.
 
For on-chain asset management whenever someone invests in an on-chain fund, the corresponding smart contract issues fund tokens and transfers them to the investor's account. These tokens represent partial ownership of the fund and allow token holders to redeem or liquidate their share of the assets.
 
In summary, the opportunities of DeFi are based on

• Efficiencies: While much of the traditional financial system is trust based and dependent on centralized institutions, DeFi replaces some of these trust requirements with smart contracts. The contracts can assume the roles of custodians, escrow agents, and CCPs.
• Transparency: All transactions are publicly observable and smart contract code can be analyzed on chain.
• Accessibility: Theoretically open to anyone, the risk of discrimination is almost inexistent due to lack of identities.
• Composability: Any two or more pieces can be integrated, forked, or rehashed to create something entirely new. Anything that has been created before can be used by an individual or by other smart contracts. This flexibility allows for an ever-expanding range of possibilities and unprecedented interest in open financial engineering.

 
The risks include:

• Smart contract execution: Users have to be aware that the protocol is only as secure as the smart contracts underlying it; leaving it vulnerable to coding errors or exploration (by analyzing the transparent underlying code).  Most users won't be able to read contract code, understand its potential security concerns, or understand the data payload - and there is no central administration to adjudicate issues or risks (although there are insurance and other similar services).

• Operational security: While blockchains are permissionless and not reliant on a central government or authority; ironically governance and some control is often held by a small groups of people (usually the project's core team) and smart contracts are reliant on external data.

• Illicit activity: Pseudonymity can be abused by actors with dishonest intentions. Regulators can (or should) regulate a decentralized infrastructure, there are two areas that deserve special attention, namely, fiat on- and off-ramps and the decentralization theater.

• Scalability: (This is why I am intrigued since by Solana which is built with native parallelism; Solana scalability >> Ethereum scalability)

 
DeFi has unleashed a wave of innovation. On the one hand, developers are using smart contracts and the decentralized settlement layer to create trustless versions of traditional financial instruments. On the other hand, they are creating entirely new financial instruments that could not be realized without the underlying public blockchain. Atomic swaps, autonomous liquidity pools, decentralized stablecoins, and flash loans are just a few of many examples that show the great potential of this ecosystem.

It was great presenting to the Notre Dame Tech Forum. 
Everyone got to tell me:

What Do I Have Wrong about
What You Need to Get Right
To Grow a Company?

The five things I think you need to get right include:
​

• Find Product Market Fit  (inspired by @ Marc Andreessen)
• Optimize Marketing / Sales Funnel 
• Create Customer Success  (inspired by Nick Mehta)
• Build Company Culture (inspired by Dan Coyle) 
• Use KPIs for Continuous Improvement (main metric from Dave Kellogg)

​Below is the presentation and here is the recording of the event. 
​

Platform and APIs can also lead to happier or at least more easily retained customers (leaving you may also mean leaving or at least changing how they interact with vendors from your partner ecosystem) and a lower cost and higher win rate for big prospects.   The customization afforded in an on-premise world can be more easily replicated for SaaS when there is a platform.  Often you have to say "no, that is not on our roadmap" to a big prospect who needs some specific capability (often for big enterprise prospects it is an integration into a legacy system). However if you have a platform your answer can more likely be "yes, you can do that via our APIs, let's show you how or introduce you to a third party integrator."  (This is certainly one of the few times you'd say "wow, the cloud is finally catching up to on-prem.")

​Later technologies such as blockchain could usher in new era of api-driven business models as Joe Liebkind contends. However much like the same way that 25 years ago we couldn’t imagine the value and capabilities the web delivers today; we have little ability to understand how the proliferation of blockchain’s distributed, trusted, transactional capabilities will impact our economy (more on this in an upcoming blog post). 
 
HOW
Whether building from scratch or undertaking the long, difficult journey to transform current (often on-premise often monolith) software to being services and API based; it's going to be tough.
 
In short, you need to consider your platform business offering like any other business offering

• What are the objectives or strategy
• How are you going to design, build, test and deliver it
• How will your customer use it
• How will you manage and maintain it throughout its lifecycle

​(Themes emphasized by Carol Russell and Forrester’s Randy Heffner)
 
Another way to consider the business plan for an API or platform is the API Model Canvas (created by Manfred Bortenschlager based on the Lean Canvas).

Platform strategy can also be considered through the lens of developer experience.  In an often quoted 2012 speech by John Musser at the O’Reilly Open Source Convention, he said that the
​5 Keys to a Great API are

• Provide a valuable service
• Have a plan and a business model
• Make it simple and flexible
• It should be managed and measured
• Provide great developer support

Every platform requires developers; here are a few tips to provide great developer support and build a vibrant developer community

• Develop inbound SEO acquisition and brand to attract developers
• Create scalable content (tutorials, guides, API references)
• Build community and boost champions
• Have a dedicated developer support and onboarding team (You need people focused on the success of the big partners that your PM and BD team recruited to expand your offering.)
• Understand your audience (and not all of them will be developers. The decision makers and stakeholders in partner companies will include CROs, business  development executives, and CEOs!) 

​Platforms and Product Led Growth
Platforms can help you expand or build your offering and your company in a variety of ways: by organically or inorganically becoming a multi-product line company (good luck doing this without being a platform company), by enabling system integrators (SIs) and value-added resellers (VARs) to implement or extend your SaaS offering, by enabling product partners to provide integrated related functionality, and by allowing you to more easily integrate with supplier partners that provide functionality.​​

(or at least just listen to a select few when considering long term SaaS product strategy)

SHORT TERM SAAS PRODUCT STRATEGY
You should definitely be listening to clients, partners, internal stakeholders, prospects, and product metrics when considering what to do next with your current products - your short term product strategy.  As discussed in SaaS Product Metrics, the KPIs for these products could include win rate, customer adoption, customer NPS, short term revenue, and alignment with the your company's (short term) KPIs and metrics. 

However your short term SaaS product strategy is often focused on market penetration; it is usually a bottom's up view of how to maximize the value of your current products in your current market (your current customers and customers like them).  The Ansoff Matrix, one of the frameworks mentioned in Notes on Software Product Strategy, illustrates that there are other perspective you can consider for your SaaS product strategy.

LONG TERM SAAS PRODUCT STRATEGY
You can consider what future products you can sell into your current markets by creating a (future) product development strategy.  Also you can consider how to modify your current products to sell into future markets as part of your market development plans. (Both of these strategies are easier if you are a platform company; API-centric cloud platforms enable developers to more quickly build complementary products).  
As part of this longer term product strategy you should consider 

• Company vision and longer term company strategy
• Market forces (For example conside Porter's Five Forces framework which is discussed in Notes on Software Product Strategy:  competitive rivalries, power of suppliers, power of customers, threat of new entrants, threat of substitute products). 
• Competitors in these new markets and for these new products
• Ideas from a few of your best and most visionary customers about
  • how your core capabilities can provide value in other parts of their business (future product development)
  • how modified versions of your products can help these customers grow in other geographic / vertical markets.

In other words, you need to build your perspective on a given market and how your offerings and distinct value proposition fits into this ecosystem. 

And remember you need short term deliverables for longer term strategic goals agreed upon across your company as there will always be pressure to use that capacity to accommodate short term revenue requests.  There will always be an inherent tension between the Sales team who is paid to deliver monthly and quarterly results and the Product team who is paid to consider quarterly and yearly impact.  

And there will always be less capacity than you think.