AIAgentDevelopment
Agents that plan and execute multi-step work are one of the highest-leverage and highest-risk categories in applied AI right now. We build agents scoped tightly enough to be reliable, with the guardrails and human checkpoints that make autonomy safe to deploy.
Avg. workflow completion rate
Avg. manual steps eliminated
Agents in production
Agent hype has run well ahead of agent reliability, and we design around that gap rather than pretend it doesn't exist. A general-purpose agent asked to do anything is unpredictable; a tightly scoped agent asked to do one specific multi-step workflow, with clear tools and clear boundaries, can be genuinely reliable in production.
We start every agent engagement by decomposing the target workflow into discrete steps, deciding which ones the agent can execute autonomously and which need a human checkpoint. Insurance claims and legal contract workflows, for example, tend to need more checkpoints than a logistics routing task where the cost of a mistake is lower and more reversible.
Tool use is where agent engineering actually lives. We give agents a well-defined, limited set of tools, whether that's querying a database, calling an internal API, or drafting a document, and we build extensive logging so every decision the agent made is traceable after the fact, not a black box.
VectorLogix's route optimization system and Meridian Insurance's claims automation both run on agent architectures built this way: narrow scope, explicit tool access, and human review at the points where a mistake would actually matter. That discipline is why they're still running in production rather than quietly disabled after a bad incident.
What'sincluded
Workflow Decomposition
Breaking a complex multi-step process into discrete, agent-executable steps with clear boundaries around autonomy versus human review.
Tool Use & Function Calling
Well-scoped tool integrations, from internal APIs to database queries, that give agents specific capabilities rather than open-ended access.
Multi-Agent Orchestration
Coordinated systems of specialized agents for workflows too complex for a single agent to handle reliably alone.
Guardrails & Human Checkpoints
Explicit rules and review points that keep an agent's autonomy matched to how reversible and consequential each action actually is.
Decision Logging & Traceability
Full logging of every agent decision and tool call, so behavior can be audited, debugged, and explained after the fact.
Engagementprocess
Workflow Mapping
We map the target workflow step by step and identify where an agent can add real leverage versus where human judgment stays essential.
Scope & Guardrail Design
We define exactly what tools the agent can access and where human checkpoints sit, based on the cost of a mistake at each step.
Build & Simulation
We build the agent and test it extensively against simulated and historical scenarios before it touches live workflows.
Supervised Pilot
We run the agent in production with heavy human oversight, expanding autonomy only as reliability is proven with real data.
Scaled Autonomy
We gradually reduce required human checkpoints where the data supports it, while keeping logging and audit trails in place permanently.
Toolswereachfor
Relatedcasestudies
Industriesweapplythisin
Commonquestions
By scoping its tool access tightly, requiring human review at the highest-consequence steps, and testing extensively against historical scenarios before any live deployment. We expand autonomy gradually as production data proves the agent reliable, rather than launching at full autonomy on day one.