Use Bitquery routing workflows
Give your agent real actions with Bitquery routing workflows without losing control.
3-day free trial · No charge during trial
Use cases
Pairs well with
Why it matters
The practical reason to use it.
Bitquery is not just another integration toggle.
How it works
A step-by-step look at the workflow.
Step 1
Start with the live reporting flow where Bitquery should stay visible inside the conversation instead of hidden in a separate portal.
Step 2
Connect Bitquery to credential controls and embeds so the agent can read the right context before it answers and write back the.
Step 3
Define which agents can use Bitquery, which actions are approved, and where routing workflows should stop for human review.
Step 4
Review the conversations that used Bitquery, tighten the prompts and access rules, and expand from live reporting to metric lookups only after.
Agent action
What the tool lets agents do.
Live workflow context
Bitquery routing workflows for AI agents keeps live workflow context connected to the conversation.
Next-step execution
Bitquery routing workflows for AI agents keeps next-step execution connected to the conversation.
Context-rich records
Bitquery routing workflows for AI agents keeps context-rich records connected to the conversation.
Production-ready follow-through
Bitquery routing workflows for AI agents keeps production-ready follow-through connected to the conversation.
Safety controls
How to keep actions scoped.
Scoped agent access
Bitquery routing workflows for AI agents keeps scoped agent access connected to the conversation.
Channel consistency
Bitquery routing workflows for AI agents keeps channel consistency connected to the conversation.
Prompt and policy guardrails
Bitquery routing workflows for AI agents keeps prompt and policy guardrails connected to the conversation.
Review loop
Bitquery routing workflows for AI agents keeps review loop connected to the conversation.
What you get
The changes teams should notice first.
- Faster routing-heavy conversations with Bitquery connected to the same agent workflow
- Less copy-paste because Bitquery keeps the next step attached to the conversation context
- Cleaner execution paths when Bitquery carries the right owner, record, or status forward
- A clearer path from question to action without another dashboard hop
What our users say
Businesses use InsertChat to launch branded assistants faster and keep their knowledge in one branded AI assistant.
Finally, one place for all my AI needs. The ability to switch models mid-conversation is game-changing.
Sarah Chen
Product Designer, Figma
We deployed AI support in 20 minutes. Our response time dropped by 80%. Customers love it.
Marcus Weber
Head of Support, Notion
The white-label option let us offer AI services to our clients overnight. Revenue grew 40% in Q1.
Elena Rodriguez
Agency Founder, Digitale Studio
Commonquestions
Open any question to see a short, plain answer.
InsertChat
Product FAQ
Hey! 👋 Browsing Bitquery routing workflows for AI agents questions. Tap any to get instant answers.
Bitquery routing workflows for AI agents FAQ
How does InsertChat use Bitquery in production?
InsertChat uses Bitquery inside a live agent workflow so the conversation can read the right context, trigger the right action, and keep the next step attached to the same thread. The goal is to make live reporting faster and cleaner, not just to expose another app connection. When the workflow is set up well, the user gets a better experience and the team gets less manual cleanup.
What should teams connect before launching Bitquery?
Teams should connect credential controls and embeds plus the rules that define what the agent can do with Bitquery before launch. That keeps the assistant grounded and makes the rollout feel operationally complete instead of half-wired. Starting with one bounded workflow is the fastest way to see whether the integration is actually reducing manual work.
Can a human step in when Bitquery is not enough?
Yes. InsertChat is designed so the agent can handle the repetitive layer and then pass the conversation, with context, to a human when the request needs judgment or an approved exception. That makes Bitquery useful without pretending every case should stay fully automated from start to finish.
How do teams know the Bitquery rollout is working?
Teams know the rollout is working when metric lookups now resolves faster, with cleaner routing and less copy-paste between systems. If the workflow is working, the same request should take fewer steps for Bitquery users and the answer should arrive with better context. The best signal is operational: less friction, not just more tool coverage.
Ready to get started?
Start your 3-day free trial. No charge during trial.
3-day free trial · No charge during trial