Service 01
Agentic Workflows
Make your business scale without adding headcount.
Service 01 · Agentic Workflows
A function that grows only by adding people has a ceiling. We rebuild it around agents that run inside the tools your team already uses, with the guardrails, fallbacks and observability that keep them working under real load.
OutcomeAgents that survive production traffic, and don’t quietly break at 3am.
Who Agentic Workflows is for
04 profiles- 01 / 04
Operations leaders running a function that scales only by hiring (support, front desk, back office) who need the headcount curve to flatten.
- 02 / 04
Teams whose first agent demo worked but won’t survive production traffic.
- 03 / 04
Operations leaders looking to automate multi-step internal workflows without a brittle script tax.
- 04 / 04
Founders evaluating whether agents are the right shape for their problem at all.
How Agentic Workflows works
04 steps- Step 01
Map the function, not the model
Failure paths defined before any code is written
Walk the workflow end-to-end with the people who run it today, draw a hard boundary around what the agent owns, and define the failure paths before any code is written.
- Step 02
Workflows and evals before any agent
A labelled eval set, built from real traffic
The response SOPs become explicit if/else workflows, and a labelled evaluation dataset is built from real traffic. Only then are agents built, and tuned to a target accuracy.
- Step 03
Build with guardrails first, instrument before launch
Guardrails ship before the happy-path demo
Tool-use schemas, structured outputs, retries, timeouts, and human-in-the-loop checkpoints, shipped before the happy-path demo. Traces on every step, evals on the orchestration policy, cost ceilings per run, and alerts that fire on loop or stall conditions.
- Step 04
Operate inside the tools the team already uses
Nobody has to change how they work
Each agent runs in the software that function already uses, so nobody has to change how they work. Then: weekly review of failure cases, regression evals on policy changes, and cost-per-task reduction as the workflow stabilizes.
What you get
05 deliverables- D-01
Agents deployed inside the tools the function already uses: no re-platforming and no new interface to learn.
- D-02
A production-deployed agent with documented tool contracts and guardrails.
- D-03
Observability stack: traces, evals, cost dashboards, alerting.
- D-04
Runbook covering common failure modes, retry policy, and human escalation.
- D-05
A labelled evaluation dataset that stays with you, so accuracy can be re-measured after any change.
Where we've shipped this
04 engagementsFrequently asked questions
06 questionsWhich business functions can actually be automated?
The ones with a repeatable decision and a written or inferable SOP. We have shipped into customer support and shared inboxes, front-desk and appointment prep, marketing and brand content production, and R&D / quality control in a testing laboratory. The test is not the department; it is whether someone can describe the decision rule, and whether there is enough historical traffic to build an evaluation set from.
How is agentic automation different from regular workflow automation?
Regular automation follows fixed steps. Agentic automation makes decisions: which tool to call, when to ask for human input, when to give up. That flexibility is also where it breaks. We add the guardrails, fallbacks, and observability that keep an agent’s decisions honest under real load, with hard limits on cost and iteration count.
How much of a function can you realistically automate?
Not all of it, and we scope against that. On a D2C support inbox, 74% of drafted replies went out with no edit at all and average handling time fell from 8 minutes to 2-3; the remaining share still needs a person, and the system is designed to route it to one cleanly. The number that matters is the share of volume a human never has to touch, measured after launch, not the share a demo can handle.
What does production-grade really mean for an agent?
It means the agent has a runbook for the on-call engineer at 3am, evals that catch behavioral regressions before users do, fallbacks for when tool calls fail, observability for every decision the agent makes, and cost limits so a runaway loop does not invoice you for thousands of tokens. Demo-grade has none of these.
How do you keep an agent from looping or burning tokens?
Hard limits on iteration count, token budget, and tool-call depth, enforced before the agent starts. Observability that flags when the agent is approaching a limit. Fallbacks that escalate to a human or a deterministic path when the agent gets stuck. Evals that catch loop-prone prompts before they reach production.
When is an agent the wrong shape for a problem?
When the workflow is fully deterministic, an agent adds latency, cost, and failure surface for no upside. Use a script. When the decisions need real human judgment with high stakes, an agent should be assisting a human, not replacing one. We turn down agent work where a simpler tool would do the job better.
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