LocusPhiMulti-step agents that plan, use tools, remember context, and act with approval built in.
Some work doesn't fit a single request-and-response step — it takes a sequence of decisions, tool calls and system actions, informed by what's already happened earlier in the task. We design and deploy agents for exactly that: systems that plan a multi-step approach, call the tools and systems they need, carry memory or state across the steps, and stop for approval at the points you define.
The workflow
A single AI call can answer a question or classify a document, but it can't independently plan and carry out a task that spans several steps, tools and decisions — research something, check it against three systems, draft an output, and route it for approval, remembering context the whole way through. That kind of work still gets stitched together by hand, one step at a time, by a person.
How a multi-step agent plans, acts and stays inside approval boundaries
Where it fits
Illustrative applications across common business functions — not a list of confirmed client deployments.
Before
- A person manually stitches together several tools and systems per task
- Context has to be re-explained at every step
- No consistent record of what was checked or why
- Multi-step work bottlenecks on individual availability
- Errors compound across steps done by hand
After
- The agent plans and executes the full multi-step task itself
- Memory/state carries context accurately across steps
- Every step, tool call and decision is traced
- Approval gates sit exactly where you define them
- The same task runs consistently, at any volume
How we deliver it
Design
We map the task into explicit steps, decision points, required tools and where approval must sit.
Build
We build the agent as a structured, inspectable graph — tool access, memory/state and reasoning steps defined explicitly, not left to a single prompt.
Guardrail
We configure approval gates, policy constraints and scope limits so the agent operates strictly inside the boundaries you set.
Evaluate & Deploy
We test the agent against real and edge-case scenarios, then deploy it with full tracing in place.
Operate & Improve
We monitor how the agent performs in production and refine its tools, memory and reasoning as real usage reveals gaps.
Technology choices
The specific tools and frameworks we use to build and run this — selected per project, not fixed to one vendor.
Agent frameworks
Tools & function calling
Memory & state
Observability & evaluation
What changes for your team?
These are value dimensions we design for, not guaranteed figures. Any metric shown elsewhere is illustrative unless labelled as based on client or project data.
The agent plans and acts, but only ever inside the tools, scope and approval boundaries your team defines — every step traceable, nothing improvised outside that boundary.
Works with your systems
Configured for your business
We configure this solution around the way your organisation already operates.