How GibsonAI Uses AI Agents to Automate the Worst Parts of DBOps

Harish Mukkami, CEO of GibsonAI, believes that providing devs with the ability to set up and manage databases in seconds keeps apps scalable and devs free from the headaches that come with managing back-end chaos. His AI-powered database management solution is up for the task.

With its name inspired by the iconic supercomputer from the ‘90s cult classic Hackers, GibsonAI is designed to be the reliable back-end brain for devs and their agents.

GibsonAI flips the script on database ops (DBOps), Harish explains. If setting up a database on AWS feels like it’s dragging on for too long, GibsonAI hits fast forward. But GibsonAI isn’t just about speed — it’s like having your own AI sidekick that helps get your back end up and running. Tasks that once needed complex config menus and hours of setup can be boiled down to prompting an AI assistant.

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“As apps evolve, teams avoid touching the database because changes are hard — pushing fixes into the app layer instead. That’s how you rack up tech debt.”

Harish and his co-founder Mike have lived the pain of back-end evolution firsthand. Early versions of a product start with clean schemas, but as UI and business logic evolve, the database is often left behind. Why? Because updating database logic is slow, risky, and tedious. Most teams avoid it, opting to patch the app layer instead. It works short term, but over time, that workaround turns into serious tech debt that slows down development velocity.

With GibsonAI, all you need to do to run a complex database-related task is chat with the agent in the GibsonAI web application or connect to the service via an MCP server. Users can cook up and deploy databases in mere seconds. There’s no need for SQL wizardry, as their AI agents automatically generate SQL queries while also providing AI-driven optimization recommendations, syncing table schemas, and keeping the back end snappy.

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“How do you build AI to work with databases that have a bit more guardrails and rules and expect more deterministic execution? This is what inspired us to bring AI to the database realm.”

AI agents are getting things done in milliseconds, so your database shouldn’t be the plot twist that kills the agents’ momentum. The only “movie magic” Harish is trying to decipher, as he puts it, is how to make AI play by a database’s rules while delivering reliable, deterministic results when AI agents tend to be rather unpredictable.

AI has fundamentally changed who developers are building things for. For a long time, back-end tools were mostly just used by humans. However, AI agents have flipped the script by becoming both the user and the consumer: Many agents are capable of making decisions and carrying out complex tasks without human input. According to Harish, it’s time for organizations to start thinking about DBOps and having a persistent layer synced up to operate at their agents’ speed.

You may recall our chat with Thor from ElevenLabs, where he mentioned structuring documents in an AI-friendly way for agents. DevRel isn’t just catering to developers anymore — it’s more about making your developers and their AI agents problem-solving heroes in their organizations.

This is precisely the playground GibsonAI is operating in, Harish explains. He and his team are working on ensuring the back end can keep up and deliver for all kinds of user demands, no matter who’s on the other side.

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“We’re provisioning the database for you, assisting you with schema design, providing the ORM to access this database, and deploying it and managing the infrastructure for you. So, you can just focus on building your front end and business logic.”

GibsonAI primarily caters to three sets of customers:

1. The zero-to-one builders, such as indie devs and early-stage teams cooking up new apps. This group wants to skip most of the grunt work and get straight to building.

2. Company teams that bring in their own databases. GibsonAI provides this group with the “Midas touch” by helping handle schemas, design, migrations, and query tuning on their own cloud.

3. Agents that can set up databases on their own. GibsonAI can easily handle the schema changes for when new fields are added, so the agent isn’t held up waiting for human inputs.

With agents taking over and becoming a force multiplier, Harish claims that a single dev can now do the work of a whole back-end team. For its own back end, GibsonAI doesn’t just use one back-end agent but a whole group of sub-agents. Each one performs its own set of tasks in parallel and responds back to a supervisor agent, which is responsible for orchestrating the tasks at the top level. So, multiple agents are responding to user queries and creating and deploying back-end systems in sub-second time rather than a single agent slowly juggling everything.

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"We wanted to offload model selection to us so that developers don’t have to figure out which model is best for a task. We make the decisions for them.”

GibsonAI assigns different foundation models to different agents based on each model’s strengths and reduces the guesswork associated with picking the right LLM for the job. Harish brings up Claude and ChatGPT, explaining how devs won’t have to pick and swap between different chatbots, as GibsonAI handles it all behind the scenes. Harish also hints about being open to letting devs bring their own models to the table down the line.

Looking ahead, GibsonAI wants to squeeze out faster speeds from queries and databases and to build agent memory. This would enable replaying steps that agents went through in the past and make debugging, evaluation, and moving between agent chains easier.

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“The ultimate vision for GibsonAI is to power the data layer for every single AI agent. So agents are working with GibsonAI all day, and this one layer manages all their data. This way, agents won’t have to worry about whether their data is stale or the most updated information.”

If Harish had a magic wand, GibsonAI would have the resources to immediately solve all of the problems AI agents face in the back end and be the default data layer for every AI agent out there, from structured to unstructured and others in between. It looks like they’re well on their way, but without that magic wand, it might take just a bit more work to finish everything. Regardless, it’s a task that Harish and the GibsonAI team aren’t shy of taking on.

Harish states that many new products coming from startups are only there because the companies have the means to develop such functionality and not because customers have asked for it. This approach leads to a limited return on investment outside of their specific use case. His view is that the winners in the AI space won’t be flashy models but tools that can constantly adapt to dev needs instead of passively serving a fixed role.

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“One of the challenges on the enterprise side is that people aren’t seeing a lot of ROI outside of a subset of use cases. Developers are building a lot of products. But the question is: Are they really solving the right problem? Are they really solving a business problem that is going to have a material impact?”

Harish believes that, at this point, we mostly know what models can and can’t do. Unless there’s a leap as significant as the breakthrough in transformer architectures in 2019 and foundational models reach that coveted “AGI” status, the incremental updates to the AI ecosystem will be easy to follow. According to him, this puts a lot of emphasis on how AI is actually used.

When you work with software, AI can write its own code, test it, and even swap in different models on the go. But when we move from the digital to the physical world, you don’t get do-overs. If you plan on making a material impact, AI’s results need to be deterministic. Speaking of “material impact”, Harish also acknowledges that a lot of digital problems remain unsolved, and AI’s full impact on the physical world is still untapped.

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“It’ll be interesting to see how AI evolves and changes the physical world. Right now, we’re just making everything digital, more efficient, but it’s all still kind of trapped within the internet, within computers.”

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“We want GibsonAI to be the always‑on brain for every AI agent — efficient, reliable, and never the bottleneck.”

Agents may be the future of application development, but they still need a back end that can keep up with the pace of work (the beauty of the baud!), be it setting up databases, tuning them, or migrating database contents.

GibsonAI is bringing an agent-heavy mindset to database ops and neatly wrapping it in DevRel DNA that ensures developers clearly understand what’s happening and why. It has helpful documentation with use cases and guides to get you up and building.

Harish (you can find him on LinkedIn) is giving developers the means to be faster and smarter, and they’ll soon have more to play around with. GibsonAI and Appsmith are cooking up something new together. While details are still under wraps, expect something very, very cool!