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Autonomous agents and workflow automation.

Right now, somewhere: an agent is answering support tickets at 2am.

Under the hood

Everything an agent needs, built in.

Agent orchestration

Tasks routed across a graph of agents, each handling the part it is best at.

Cost & token monitoring

Every model call metered, so the cost per run is a number you watch, not a surprise.

Tokens/min12.4k
+8%prev
Cost/run$0.042
-3%prev

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TUE

WED

THU

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SAT

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Live activity feed

A running log of what each agent did, which tools it called, and what it retrieved.

Plannerdone

Decomposed task into 4 sub-goals

0.2s
Researcherdone
Coderrunning
Reviewerwaiting
Writeridle

Grounded knowledge

Agents answer from your docs, codebase, and conversations, not from guesses.

Namespaces

codebase
342
docs
218
slack
97
notion
54
Live retrieval active

Retrieval Log

codebase0.2s

vector embeddings auth module

docs7.2s

Claude tool-use schema

notion5.8s

Q3 roadmap — agent features

slack4.0s

deployment discussion #eng

Tool-call inspector

Every tool call tracked for latency and success, across all of your agents.

14Calls
web_search280ms
8Calls
code_exec1.2s
22Calls
file_read12ms
31Calls
vector_query95ms

How it works

From first call to live, in four steps.

01

Find the workflow

We pick the repetitive, high volume task where an agent pays for itself the fastest.

02

Design the guardrails

Tools, prompts, and escalation paths are defined before the agent ever touches real data.

03

Build and evaluate

We build the agent and score it against real cases until it is genuinely reliable.

04

Deploy and monitor

Live in your stack with logging, evals, and a human in the loop where it actually matters.

Integrations

Connect agents to the tools you already use

Agents reach into the apps your work already lives in, from your inbox and calendar to your CRM and knowledge base. Hover a tool to trace the connection.

Your agent

Software that does the work instead of waiting for someone to do it. Agents that qualify leads at 2am, answer the same support question for the thousandth time, and move data between the tools that refuse to talk to each other.

Customer support agents trained on your knowledge base

Lead qualification and enrichment pipelines

Workflow automation across your existing stack

Document and data processing at volume

Guardrails, evaluation, and human-in-the-loop escalation

FAQ

Questions, answered.

It handles the repetitive work: qualifying leads, answering the same support questions, and moving data between tools that do not talk to each other.