Put agents to work across your tools.
We build agents for defined tasks across your tools, with people approving sensitive actions.
Prepare this supplier request.
- Read the request
- Gather context
- Prepare a draft
The supplier details and supporting documents are gathered. A response is ready for the team to review.
Waiting for human approval
What we build around the task
We build an agent around one job your team needs done, such as gathering records and preparing a request for review. We limit what it can read or change, check important steps and give your team a clear way to intervene or recover when a run fails.
For tasks that require decisions across several systems, such as research, triage or preparing work for a team to approve.
- Sort incoming requests
- Gather and prepare
- Act with approval
One clear task
We agree on the job, the tools it may use and when it must stop. We keep the task small enough to check.
Controlled tool access
We connect the systems it needs, limit what it can read or change and require approval before consequential actions.
Checks and recovery
We check tool results, record progress and recover from failed calls without repeating changes blindly.
A view into every run
We give your team a view of completed tasks, failed runs, human intervention and cost so you can judge whether the agent earns its place.
Example workflow
Prepare a request for approval
For an operations team that gathers information across tools before making a decision.
Receive the request
A new request starts a run with a defined goal and allowed tools.
Gather and validate
The agent finds the relevant records and checks that required information is present.
Hand over a proposal
A person reviews the prepared action before the system makes a consequential change.
What we verify: Tool failures, missing information and repeat requests have explicit recovery paths.
Agent or workflow?
Agent or workflow?
A fixed workflow is often the better choice when the steps are already known.
Repeat the same steps every time
- Consider
- A workflow
- Why
- Predictable execution is easier to test and support
Choose different tools based on the input
- Consider
- An agent
- Why
- The task needs decisions within clear boundaries
Approve a payment or change sensitive data
- Consider
- A human approval step
- Why
- A model's decision alone is not enough
Answer a question without taking action
- Consider
- An AI feature
- Why
- A conversational interface may be all you need
| Your task | Consider | Why |
|---|---|---|
| Repeat the same steps every time | A workflow | Predictable execution is easier to test and support |
| Choose different tools based on the input | An agent | The task needs decisions within clear boundaries |
| Approve a payment or change sensitive data | A human approval step | A model's decision alone is not enough |
| Answer a question without taking action | An AI feature | A conversational interface may be all you need |
How we work together
Map the task
We follow a real request through your current process and agree what a successful run should produce.
Build the first working path
We connect only the tools the task needs, add checks between actions and place approval before sensitive changes.
Test failure and recovery
We test missing information, tool errors, repeated requests and spending limits before giving the agent live work.
Launch with oversight
We review completed and failed runs with you, then expand the agent's role when the results support it.
Common questions
Can you improve an agent we have already built?
Can you improve an agent we have already built?
Yes. We start with actual runs and failure traces, then review the task design, tool contracts and recovery behavior before changing models or prompts.
Will the agent run without a person watching it?
Will the agent run without a person watching it?
That depends on the task. Routine, reversible actions can be automated; sensitive actions may need approval. We agree those boundaries before launch.
Would a better model fix our agent?
Would a better model fix our agent?
Sometimes, but many failures come from missing context, unclear tool responses or too many dependent steps. We measure those causes before switching models.
How do you control agent running costs?
How do you control agent running costs?
We set limits on steps, retries, concurrency and spend, and report cost per completed task alongside failures and manual intervention.
Which task should an agent take on?
Bring a real workflow. We'll map the decisions, the tools and where people should stay involved.
or reach us at hello@phaseahead.ai