Function-mapped workflows beat “AI as a teammate.”
Designing agents to mimic humans bloats every flow: more hops, more glue logic, more places to fail. We design for machines—schema-first, tool-first, governance in code—so intent goes straight to action.
What goes wrong with “AI employees”
Dressing models up as junior teammates recreates office friction: DM chains, fake dashboards, and brittle routing layers. Latency stacks up and success rates tank because nothing is enforced in code.
AI doesn’t need onboarding or org charts. It needs access, contracts, and observability. Once you drop the metaphor, workflows shrink, cost drops, and behaviour becomes predictable.
Human-mimic: export → paste → ask → copy → paste → tag (6+ hops).
Function-mapped: fetch_highlights(source) → write_summary(destination, tags).
Structure, retries, and auditing live in code; the model just fills the schema.
Rules we ship with every agent
These mirror our 12-factor approach and underpin voice agents, copilots, and multi-agent meshes we deploy.
Behaviour lives in architecture, not prompts
We enforce behaviour with typed tool calls, reducer-managed state, and audit-friendly logs. Prompts set intent; code sets the guardrails. That’s why our agents stay deterministic in production.
• Prompts are versioned and traceable.
• Context windows are explicit and inspectable.
• Tools have signatures, retries, and user-facing errors.
• State is replayable; governance is codified.