Coinbase is experimenting with a new generation of AI agents that go beyond answering individual prompts. The company has introduced CEEcil, an internal AI system designed to operate more like an always-available engineering teammate.
Unlike conventional AI assistants that start with a blank context for each interaction, CEEcil can remember previous discussions, retrieve operational knowledge, monitor selected Slack channels and follow up on unresolved conversations.
Coinbase detailed the system's architecture in an engineering post published Aug. 28.
For the broader Base ecosystem, the experiment highlights how Coinbase is thinking about AI agents, persistent context and automated workflows that could eventually become increasingly important across onchain applications.
CEEcil Is Designed to Act Like a Teammate
Coinbase built CEEcil around three main principles: it should communicate like a coworker, retain useful context and know when to reject an inappropriate request.
The system can be mentioned directly in Slack and provide answers based on previous team discussions, internal documentation and current operational data.
It can also proactively participate in approved channels by joining relevant threads, reacting to messages and following up when a conversation appears to have been abandoned.
Coinbase says these behaviors are deliberately limited by rate controls and can be disabled through operational kill switches.
Three Layers Give CEEcil Long-Term Memory
The most interesting part of the system is its memory architecture.
CEEcil uses three layers:
1. Short-Term Observation
A background process monitors a limited set of approved Slack channels and extracts durable information such as decisions, blockers, ownership and deadlines.
The raw observations are temporary rather than becoming permanent records.
2. Nightly "Dreaming"
Coinbase calls its nightly consolidation process "dreaming."
During this stage, CEEcil turns the day's fragmented observations into longer-term summaries. The objective is to preserve important decisions and outcomes while removing the noise generated by constant conversations.
3. Retrieval When Needed
Instead of inserting an enormous memory dump into every prompt, CEEcil retrieves only the information relevant to the current question.
It can search historical memory, inspect recent activity or retrieve information from specific services and tickets.
This approach is intended to make responses more focused while reducing unnecessary model costs.
Coinbase Avoided a Vector Database
CEEcil's knowledge base is built from Markdown files with structured metadata rather than a conventional vector database.
The system follows Google's Open Knowledge Format (OKF), which is designed to represent knowledge using portable Markdown files and YAML frontmatter.
Coinbase says the approach makes its internal knowledge easier to review, modify and track through Git.
That also gives engineers visibility into exactly what information the agent has access to.
The company acknowledges that simple text search will eventually reach its limits, but says a relatively small team-specific knowledge base does not currently justify the complexity of an embeddings-based system.
A Two-Part Architecture Keeps AI Under Control
CEEcil is split into two major components: a Go service and an AI agent runtime.
The Go service handles operational functions such as:
Slack communication
memory ingestion
routing
knowledge retrieval
background workers
state management
audit information
kill switches
The agent runtime handles more complicated reasoning and multi-step tasks.
This separation is important because Coinbase does not allow the AI model to directly control critical operational actions.
For example, if someone asks who is currently on call, the model can determine that the request requires an on-call lookup, but the Go service retrieves the actual schedule from the relevant API.
That limits the ability of the model to simply invent an answer.
CEEcil Can Also Say No
One of Coinbase's examples demonstrates why judgment is an important part of its AI-agent design.
An engineer asked CEEcil to add an operational runbook to its knowledge base. The system detected that the document contained customer-identifying information and refused to commit the complete document to source control.
Instead, it suggested alternatives such as sanitizing the document, creating a link-only reference or moving the operational information into an approved system.
This illustrates a key difference between a basic automation tool and an agent designed to operate within organizational policies.
AI Agents Could Become More Important for Base Builders
Although CEEcil is an internal Coinbase engineering system rather than a Base protocol feature, its architecture points toward a broader trend relevant to the Base ecosystem.
AI agents are increasingly moving from simple chat interfaces toward systems capable of remembering context, retrieving information, making decisions and taking actions.
For builders working on Base, that could eventually translate into agents that monitor onchain activity, manage operational workflows, interact with decentralized applications or coordinate transactions based on predefined rules.
The combination of AI agents and onchain infrastructure could also create new applications where software does not simply recommend an action but executes it.
Results Show the Value of Persistent Context
Coinbase says CEEcil has already helped reduce the amount of time engineers spend waiting for specialized knowledge.
Simple knowledge-based requests can reportedly be answered within seconds, while more complex agent tasks generally take a few minutes.
The company also said CEEcil helped triage and mitigate a low-severity production incident by providing relevant context and analysis.
The system has even been able to identify and fix a bug in its own memory-digest process before opening a pull request for human review.
Importantly, Coinbase still requires humans to merge changes created by CEEcil.
Why This Matters for Base
Base has increasingly positioned itself as an environment for developers building consumer applications and emerging onchain use cases. AI agents could become another important category as software becomes capable of interacting with blockchain infrastructure autonomously.
CEEcil shows one possible blueprint: persistent memory + live data + controlled tool access + human oversight.
The experiment also demonstrates that building useful AI agents may be less about giving models more context and more about giving them the right context at the right time.
For Base builders, that distinction could become increasingly important as AI agents move from experimental chatbots toward software capable of participating in real workflows and eventually interacting with onchain applications.
Conclusion
Coinbase's CEEcil experiment highlights a shift from disposable AI assistants toward persistent digital teammates.
By combining long-term memory, recent context, deterministic APIs and controlled AI reasoning, Coinbase is attempting to create an agent that can remain useful without becoming unpredictable.
As AI agents increasingly interact with financial and onchain systems, architectures like CEEcil could offer a blueprint for building agents that are not only more capable, but also more auditable and controllable.
For Base, the bigger opportunity may be what happens when these persistent AI agents begin moving beyond internal workflows and start interacting directly with the onchain economy.
