AI agents that
do the work
Not another chatbot. Spojit agents plan a sequence of steps, call your tools, observe the results, and recover from failure. They run as steps inside durable workflows, backed by frontier Claude and Gemini models.
- PlanRefund needs the order total and the customer tier before deciding.
- Acttoolshopify.getOrder( #48213 )
- ObserveOrder $612 · VIP customer · paid by card
- AdaptOver the auto-approve limit. Route to a human approval step.
- Returned structured result
{ decision: "review" }
Plan, act, observe, repeat
Give it a goal and tools
Describe the outcome and grant the agent the connectors it may use. No step-by-step script to write up front.
It plans and acts
The agent reasons about the next move, calls a tool, reads the result, and adjusts. It loops until the goal is met.
It returns structured data
The result comes back as typed, predictable output that flows straight into the next step of the workflow.
Agents built for real work
Multi-step reasoning
Agents break a goal into steps, act on each, and adapt to what they find. They are not limited to a single reply.
Tool calling, auto-discovered
Every connected integration becomes a tool the agent can call, with the schema and auth already attached.
Persistent memory
Context carries across steps and sessions. Token-aware management keeps long runs coherent instead of forgetful.
Structured output
Agents return typed data ready for the next step. No brittle parsing of free-form text to make it usable.
Recovers from failure
When a call fails or returns something odd, the agent retries or changes approach without you writing the fallback.
Frontier models
Backed by the latest Claude and Gemini models, so the reasoning is good enough to trust with real decisions.
Put judgment in the loop
Triage and classify
Read an incoming message, decide what it is, and route it. The kind of judgment a fixed rule cannot capture.
Research and summarize
Gather from several sources, pull out what matters, and hand back a clean summary for the next step.
Orchestrate many APIs
Coordinate a sequence of tool calls across systems, deciding the order based on what each result returns.
Make the call
Apply your criteria to a real case and reach a decision, then pass it to a human approval step when it matters.
Agents vs rigid scripts
A script does exactly what you wrote it to do. An agent adapts to what it actually finds.
Reach for an agent when
- The input varies and the next step depends on what you find
- The task is several steps with tool calls in between
- You want recovery from errors without hand-writing the retries
A hand-written script means
- You code every branch and edge case yourself
- It breaks the moment an input looks different
- Every new case is another edit to the script
Give an agent a goal
Start free and drop an AI agent into your first workflow. No card needed.