How Fern Works
Fern Strategy designs and ships production-grade agentic AI systems for regulated and quasi-regulated industries. Fern doesn't pitch transformation. Fern diagnoses workflows, redesigns them for autonomous execution, and builds the infrastructure that lets your team run the new system after the engagement ends.
This is the operating model behind that work.
Guiding Principles
Map the shadow workflow, not the official one
The process diagram in the employee handbook is not the workflow. The workflow is the undocumented workarounds, the spreadsheet that catches what the system doesn't, the Slack thread where the real decision gets made. Fern maps that one, because that's the one the agent will replace.
Hunt the promising zone, not the simple zone
Simple, repetitive tasks are macros, not agents. The work that earns an agent is the complex but stable zone: workflows where the rules are explicit and durable, but the data is messy enough that humans bottleneck and rule-based automation breaks. Reconciling clinical signals across protocols. Pricing structured deals against changing inputs. That's the zone agents hold up in. That's the zone Fern hunts.
Don't pave the cow path
Most failed AI engagements take a broken human process and make it 10% faster. The 2 to 10x gains live elsewhere, in workflows redesigned from scratch for agent-first execution. Every handoff gets questioned. Every approval step gets challenged. Decision logic becomes explicit. Data becomes machine-readable. The human-shaped process gets retired, not optimized.
Track capability expansion, not vanity metrics
"Number of emails drafted" is not a return. Capability expansion is. Did the agent let the team do something they couldn't do before, screen the entire database instead of a sample, evaluate every contract instead of the top 20%, run analyses every night instead of every quarter? That's what Fern tracks. That's what scales.
Ship small. Unlock value early.
The fastest way to ruin an agentic engagement is to scope it like an enterprise transformation. Fern ships the smallest viable version of the system that produces real value, then expands from a working baseline. Three months to first production deployment is the default. Anything longer needs a defensible reason.
How Fern chooses what to work on
Most AI use cases get picked the wrong way. Whatever the vendor demoed. Whatever leadership saw at a conference. Whatever had the most internal momentum. None of those tell you where an agent creates measurable value.
Fern starts with the value chain.
Fern maps your operations stage by stage. Inputs, outputs, handoffs. Then Fern hunts for steps that are time-consuming, error-prone, or compliance-sensitive. Those are the workflows worth targeting. Everything else is a distraction.
Two zones to avoid
Copy-paste into a CRM. Form filling. Boring, repetitive, simple. A macro can handle this. Don't waste an LLM on it.
Final medical diagnoses. Closing seven-figure deals. High-stakes judgment with real consequences. Unconstrained AI here is a liability. Humans stay in the loop.
The sweet spot
Complex yet stable. Stable rules, where boundaries are rigid and don't shift randomly. Complex data, messy, unstructured, voluminous, requiring real cognitive reasoning to interpret. Reconciling handwritten clinical notes against varying lab panels. Pricing structured contracts against shifting market inputs. Triaging compliance signals across hundreds of active protocols.
Standard automation breaks when data gets messy.
Humans break when data gets voluminous.
Agents hold up on both.
Where do you sit on the scale?
Most organizations are somewhere on the journey from a tool-first operating model, where humans run workflows and AI assists, to an agent-first operating model, where agents run workflows and humans supervise. The transition is gradual, and the productivity payoff lives at the far end.
Fern works with companies in the middle three stages of this scale.
Tool-First, Curious
AI is used by individuals informally. ChatGPT in browsers. No production AI in the org. No structured strategy.
Tool-First, Active
Enterprise AI tools are deployed. Copilots, Gemini, and bots are in use across teams. Workflows are still human-shaped. No production agentic systems yet.
Transitioning
Agentic POCs are running. Specific workflows are being redesigned. Internal teams are exploring what agent-first looks like, but the operating model hasn't shifted yet.
Agent-First, Active
Production agents handle real workflows. Internal capability is emerging. The organization is hunting for architectural maturity and looking to scale agentic operations across more domains.
Agent-Native
Agent-first operations are the default. The organization architects and ships agentic systems internally. External help is rarely needed.
If you're at Tool-First Curious, the gap is foundational. Get enterprise AI tools deployed and let teams build literacy before bringing in an architect. Fern will be here when you're ready.
If you're at Agent-Native, you don't need Fern. You've already built the operating model and the internal capability. Hire from your own bench.
If you're somewhere in the middle three stages, that's where the audit-and-build model creates the most leverage. The audit phase is designed to meet you where you are.
Not sure where you sit? That's what the first conversation is for.
Agent OS
Fern designs organizations as if agents were the primary actors and humans were the supervisors. Not the other way around.
Most companies bolt copilots onto human-shaped workflows and ceiling out at 20 to 40% incremental gains. The Agent OS removes that ceiling by treating four pillars as load-bearing.
These four pillars are the destination Fern engineers toward, not a single deliverable. A Build ships the first production slice; the full operating model is where the redesign is headed.
When all four pillars are in place, the redesign can unlock step-change gains well beyond the 20 to 40% copilot ceiling, though Fern ties every engagement to a measured baseline before claiming any number.
AGENT
Five phases. Every engagement.
Audit
Two weeks, fixed fee. Fern maps the shadow workflow as it actually runs today. Triggers, handoffs, failure modes, undocumented workarounds. Quantified baseline. Fern separates the business outcome from the manual tasks currently used to achieve it.
Gauge
Fern grades every candidate workflow on impact, repeatability, and complexity, then tests for the promising zone. Most engagements surface a clear top one or two candidates and a prioritized backlog of adjacent workflows for follow-on consideration.
Engineer
Fern refactors the process for straight-through, agent-first execution. Every handoff gets questioned. Every approval gets challenged. Decision logic is made explicit. Data is made machine-readable. The output is a redesigned workflow with a named agent architecture, not a chatbot bolted onto the old one.
Navigate
Fern designs the human-agent relationship deliberately. Where does the agent act autonomously? Where does it escalate? What confidence signals does it surface? What's the off-ramp if rollout struggles? Trust is engineered through transparent intervention paths, audit logs, and graduated autonomy. Not asserted in a slide.
Track
Fern measures capability expansion against the baseline, not vanity metrics. Fern tracks adoption signals from week one. Fern tunes the human-agent split based on real failure modes, not predicted ones. The engagement ships when the metrics hold and the team can run the system without Fern.
Five Agent Roles
Working in concert.
Every agentic system Fern builds separates concerns across five roles.
Assistant
Read-only. Drafts, summarizes, answers. Talks to humans.
Analyst
Read-only. Reasons over data. Bounded scope, fewer hallucinations.
Tasker
Writes. The only role that actually does anything. Tools are strict contracts, not suggestions.
Orchestrator
Manages state, sequencing, timeouts.
Guardian
Immutable audit log, output validation, anomaly detection, escalation before irreversible actions.
The boundary between the read-only roles and the Tasker is where most production incidents come from. Fern designs that boundary first, not last.
What Fern won't do
Fern won't deliver AI when non-AI tools work.
If your workflow can be solved with Make, n8n, Zapier, or a SaaS automation, that's the right answer. Agents earn their place when no existing tool fits. The audit phase explicitly tests this before any architecture gets proposed.
Fern won't design custom when turnkey is better.
Most workflows that look agentic at first glance are solved by the AI features already built into HubSpot, Apollo, Fathom, or Microsoft Copilot. The right question isn't "what can we build?" It's "what specific workflow does this solve that an existing tool can't?" Fern tests that question first, every time.
Fern won't attempt massive system rewiring all at once.
Fern starts with minimally invasive architecture. Decoupled services, event-driven integration patterns, rather than forcing an 18-month IT overhaul.
Fern won't lock you into its tools.
Anything Fern builds runs on infrastructure your team owns and patterns your engineers can read. No proprietary runtimes, no closed model wrappers, no integrations only Fern can maintain. If Fern leaves tomorrow, the system keeps working.
Fern won't stall projects with theoretical policy debates.
Governance emerges from practice. Fern starts with a practical framework, builds trust through controlled rollouts, and co-designs guardrails as the work unfolds.
Fern won't promise returns it hasn't quantified.
Every engagement ties to a measurable business outcome. If the math doesn't work, the project doesn't ship.
Working together
Audit
Diagnostic plus build-phase roadmap. Designed as an off-ramp: if the math doesn't justify a build, the engagement ends with a clean handoff, no further commitment.
Build
Production deployment of the agent system surfaced in the audit. Anything Fern builds is designed to outlast the engagement: production patterns, audit trails, observability, documented architecture. Your team owns it from day one.
Fractional Architect
Capability stays in-house. Fern stays accessible.
The first conversation is always free, scoped, and honest about whether Fern is the right fit.