MCP TOOLS INFRASTRUCTURE
AI agents answer
MCP tools answer how do we get AI agents to do what we want
We build the infrastructure that makes AI agents reliable, coordinated, and controllable. Not wrappers. Not abstractions. The actual systems that run underneath.
The shift is already happening
By 2030, AI agents will be as common as cloud infrastructure. The question is not whether your business will use them, but whether you will control them.
AI Agent Adoption Forecast
Drag the timeline to see the future unfold.
The future is uncertain until you drag through it.
Three pillars of agent infrastructure
Every production agent system needs memory, coordination, and communication. We build each layer to work independently or together.
RAG Knowledge System
Self-improving memory that learns what good context looks like. Every retrieval trains the next one. Quality gates reject noise before it pollutes your knowledge base.
Multi-Agent Database
Persistent state that survives between agent runs. Agents share context, hand off tasks, and build on each other's work. Institutional memory for your fleet.
Agent Email Protocol
Asynchronous messaging between agents that just works. Route tasks to specialists with delivery guarantees, not brittle function calls.
RAG KNOWLEDGE SYSTEM
The Memory Crisis
Most RAG systems train themselves to be wrong.
MULTI-AGENT DATABASE
The Groundhog Day Problem
Every session ends. Every agent forgets. The same work, done over and over.
Repeated Conversations
What did we decide about pricing?
I have no record of previous conversations.
Discovery Timeline
AGENT EMAIL PROTOCOL
The Dial-Up Days Are Over
We solved this for humans in 1995. Why are your agents still living here?
Sending context... 0%
connection...
Infrastructure, not wrappers
Most agent tools add layers between you and the model. We build the systems that run underneath. The difference matters when you need to debug a production failure at 3am, or when you need to understand exactly why an agent made a specific decision.
We give you PostgreSQL tables you can query directly, message queues you can inspect, and quality models you can retrain. No black boxes. No vendor lock-in. Just solid infrastructure that you actually own.
-- memories table schema
CREATE TABLE memories (
id UUID PRIMARY KEY,
content TEXT NOT NULL,
embedding VECTOR(768),
predicted_quality FLOAT,
usefulness_score FLOAT,
tier tier_enum DEFAULT 'active',
created_at TIMESTAMPTZ
);
-- Agent mail message queue
SELECT * FROM agent_messages
WHERE recipient = 'librarian'
AND status = 'pending'
ORDER BY priority DESC;
-- Quality prediction
def extract_features(content):
return {
"length": len(content),
"has_code": CODE_PATTERN.match,
"specificity": calc_specificity(),
"source_trust": get_weight()
}hover to reveal the infrastructure beneath
Ready to build agent infrastructure that scales?
We work directly with engineering teams to design and deploy custom MCP tools. No sales calls. Just a conversation about what you're building.
Tell us about your agent stack
We will get back to you within 24 hours.