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Service 04

Agentic Workflows

Multi-step AI agents with tool use, memory, and guardrails — designed for autonomous operation at scale.

The problem

Agent demos are impressive; agents in production are dangerous without guardrails. You need something that acts on your systems reliably and stops when it should.

Our approach

We build agents with explicit tool contracts, structured memory, human-in-the-loop checkpoints on high-stakes actions, and observability so every decision is auditable after the fact.

Use cases
  • Automating a multi-step operational workflow currently done by hand, with a human checkpoint on anything high-stakes
  • Building an agent that can safely call your internal APIs and tools, with explicit contracts on what it can and can't do
  • Adding audit trails so every autonomous decision is reviewable after the fact
  • Replacing a brittle rules engine with an agent that handles the long tail of edge cases
Representative stack
Structured tool-calling with explicit contractsPersistent memory scoped to the workflow, not the whole orgHuman-in-the-loop approval steps on high-stakes actionsObservability and audit logging on every agent decision
How we engage

We scope the guardrails and approval thresholds with you before writing the agent — not after something goes wrong.

Where you can see it

ExamSurf's AI tutor is an agent operating on structured educational data — explaining questions, generating predictive practice, grading theory answers. Its actions touch a student's mastery record, so every step is traceable.

In buildExamSurf
Have a problem shaped like this?

Tell us what you’re building and we’ll scope where Agentic Workflows fits.

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