AI Agents & Workflow Automation.
Agents that complete work, with a human in the loop where it counts.
Drema builds AI agents that carry out multi-step work: reading a request, planning the steps, calling the right internal tools and returning a result a person can verify. The engineering discipline is knowing where autonomy ends — every agent we ship has defined tool permissions, spend limits, approval checkpoints and a complete audit trail.
Agents that complete work, with a human in the loop where it counts.
What usually
goes wrong.
Fully autonomous agents make compelling demos and unnerving production systems. Without bounded permissions, an agent that misreads intent can take real actions on real records, and without an audit trail nobody can reconstruct what happened or why.
Everything that ships
with this work.
Not a menu to choose from. Each piece is here because leaving it out is what makes this kind of project fail six months later.
Tool and action design
Defining the exact set of operations the agent may perform, each with validated inputs and reversible semantics where possible.
Planning and execution loop
Decomposing a request into steps, executing them, and recovering when a step fails.
Permission boundaries
Scoped credentials so the agent can only touch what its task requires, never the whole system.
Human approval checkpoints
Explicit confirmation gates before irreversible or outward-facing actions.
Audit trail
A complete record of what the agent decided, what it called and what changed, reviewable after the fact.
Cost and loop protection
Step ceilings, timeouts and spend caps so a confused agent cannot run indefinitely.
The order matters more
than the tools.
Most of what separates a project that lands from one that stalls is sequence. This is the order we work in, and why each step comes where it does.
- 01
Map the workflow
We document how a competent person does the task today, including the judgement calls. Those become the checkpoints.
- 02
Define the tools
Each action the agent can take is a validated function with clear boundaries, not open access to an API.
- 03
Shadow mode
The agent proposes actions without executing them, and we compare its decisions against your team's for a real period.
- 04
Supervised autonomy
It executes low-risk steps automatically and escalates the rest for approval.
- 05
Widen carefully
Autonomy expands only where the audit record shows sustained accuracy.
Where this gets
put to work.
The situations this service is built for. If one of these sounds like your problem it is worth a conversation — and if none of them do, say so on the call and we will point you at what would actually fit.
Ticket triage and resolution
Classifying incoming requests, gathering context and drafting or applying the fix.
Data reconciliation
Comparing records across systems, flagging mismatches and proposing corrections.
Research and enrichment
Gathering information across sources and returning a structured, cited summary.
Back-office operations
Multi-step processes across internal tools that currently consume hours of manual clicking.
Have a use case that is not on this list? That is usually the interesting one.
Chosen to fit,
not to impress.
We pick tools that suit the problem and that your team can maintain after we hand over — never to pad a capability list.
Questions we
get asked.
Straight answers, including the ones that talk you out of work we would otherwise be paid for.
What is an AI agent, in practical terms?
A system that receives a goal, decides which steps and tools are needed, executes them, and reports back — rather than answering a single question. The difference from a chatbot is that it takes actions in your systems.
Is it safe to let an agent act on our systems?
It is safe when the boundaries are engineered rather than assumed. We scope credentials to the minimum the task needs, require human approval for irreversible or outward-facing actions, cap steps and spend, and log every decision for review. We do not ship unbounded autonomy.
What happens when the agent gets it wrong?
It should fail visibly, not silently. Failed steps are surfaced with the reasoning trace, reversible actions can be rolled back, and the case is added to the evaluation set. Shadow mode before launch is what keeps the error rate known rather than discovered.
Will this replace our team?
In the engagements we run it removes the repetitive middle of a workflow and routes judgement to people. The realistic outcome is the same team handling significantly more volume, with the ambiguous cases getting more attention rather than less.
How is this different from traditional automation?
Rule-based automation breaks on inputs nobody anticipated. An agent can interpret unstructured requests and adapt its plan, which is why it suits workflows with messy inputs where a rigid script has always failed.
How long does an agent project take?
Shadow mode on a single well-defined workflow is typically reachable in four to six weeks. Expanding autonomy is deliberately gradual after that, paced by what the audit record shows.
Talk it through with a founder.
Bring the actual problem. You will get a straight answer on whether ai agents & workflow automation is the right approach
— including when it is not.
