Agents & automation
We work with Philadelphia-area teams on agent and automation workflows that handle approvals, exceptions, handoffs, monitoring, and team ownership after launch.
Agent demos are easy. Durable automation requires process judgment: where the AI is allowed to act, when a person approves, what happens when confidence drops, and how the system is observed over time.
We help teams identify workflows worth automating, design the control points, build or guide prototypes, harden the production path, and leave behind runbooks and ownership notes.
Common engagements include internal knowledge workflows, intake and triage, research assistance, sales or operations support, document-heavy processes, and tool-using agents that need measurable guardrails.
The best first workflows have known inputs, visible review points, and a clear definition of done. The agent may draft, retrieve, classify, route, or prepare work, but the operating design decides what it can change, whom it alerts, and when a human has to approve the next step.
For high-value workflows, the hard parts are not only model quality. The system needs scoped permissions, traceable tool use, exception handling, recovery steps, evaluation cases, and a team that knows how to operate it after the handoff.
Answers
A mapped workflow, clear agent responsibilities, tool and permission boundaries, approval points, fallback behavior, monitoring notes, and handoff material so the internal team can operate the workflow after launch.
Start with repeatable work where inputs, reviewers, and success criteria are already understood: intake, triage, research support, document processing, support workflows, internal knowledge tasks, and handoff preparation.
Limit tools and data access by role, define what the agent may decide versus prepare, require human approval for risky actions, log every meaningful step, and create fallback paths for low-confidence or unexpected situations.
Measure task completion, handoff quality, exception rate, approval reversals, latency, cost, failure modes, and whether the workflow improves the business process without creating hidden review work.
Yes. Many engagements start with a promising prototype that needs clearer architecture, permissions, tracing, evaluation, recovery behavior, runbooks, and an ownership model before the workflow can be trusted in production.
Production agents need task-level evals, not only demos. For retrieval quality, tool use, and answer quality, the work often connects directly to LLM, RAG, and evaluation systems.
Next step
Bring the process, exceptions, and ownership questions into a focused first call.
