I don’t want an AI that chats. I want an AI that gets things done.
That’s the promise of agentic AI — not “smart tools,” but systems that act. Book the flight. File the expense. Ship the pull request. Close the ticket.
We’re entering the concierge era: vertical agents that own an outcome end-to-end. The catch? Most agents still crumble under real-world constraints — latency, reliability, data access, and the cost to serve.
This is where founders win. Build with compute reality in mind. Design for trust. Nail a wedge where throughput and margins compound.
Why now (and why it’s hard)
Three shifts make concierge agents viable:
- Long-context, tool-using models are stable enough for structured work.
- On-device + private-cloud architectures reduce privacy friction and latency.
- Teams finally treat LLMs as a component, not the whole product.
And three realities make them fragile:
- Inference is the new COGS. Margin dies without routing, caching, and smaller models.
- Task success, not reply quality, is the KPI — and it needs verifiable evidence.
- Trust is earned through scope, guardrails, and reversal safety — not vibes.
Clarity over noise.
The concierge stack (what actually ships)
Stop thinking “agent.” Think system.
- Planner: breaks a goal into deterministic steps.
- Tooling layer: APIs, apps, and browsers with typed contracts and scopes.
- Memory: short-term scratchpad + long-term customer graph (people, entities, commitments).
- Execution engine: retries, idempotency, and human handoff.
- Verifier: checks outputs against ground truth (schemas, receipts, screenshots, APIs).
- Policy guardrails: permissions, budgets, and explainability logs.
- Model router: small/fast for pattern work; big/slow for novel steps.
Quote to remember: “LLM in the loop, not LLM as the OS.”
Compute is the business model
Your pricing ceiling is set by your per-task compute floor. Tactics that move the unit economics:
- Route by difficulty: 8–14B models for routine steps; 70B+/API-only for edge cases.
- Constrained generation: JSON schemas + function calling to kill re-tries.
- Speculative + lookahead decoding: shave 20–40% latency at scale.
- KV/prompt caching: amortize recurring prompts and repeated context.
- Embedding and tool results caching: cache intent → tool → result triples.
- Distill to locals: fine-tune small models on your tool usage traces.
- Batch where it doesn’t hurt UX: research, enrichment, backfills.
If your p95 latency is unpredictable, your intervention rate skyrockets. If your intervention rate climbs, your margins vanish.
Product and market insight
Concierge agents aren’t general. They are obsessively specific:
- Scope: one job, owned end-to-end. Example wedges:
- Travel ops: rebook flights, track credits, submit reimbursements.
- Revenue ops: enrich leads, draft outreach, log CRM updates with proofs.
- Support: triage, resolve known issues, issue refunds under policy.
- Engineering chores: PR hygiene, dependency bumps, flaky test triage.
- Back-office: vendor renewals, invoice reconciliation, license cleanup.
- Proof: every step leaves breadcrumbs — receipts, screenshots, API diffs.
- Trust: permissioned access, budgets, and easy reversals.
Bold moves attract momentum. But bold scope without guardrails is chaos.
The metrics that matter
Design your dashboard around outcomes and cost to serve:
- Task Success Rate (TSR): % of tasks completed end-to-end without human help.
- Effective Cost per Resolved Task (eCPRT): (inference + tools + verification + overhead) / TSR.
- Latency SLA: p95 time-to-resolution per task type.
- Intervention Rate: % of tasks escalated to human (and why).
- Tool Reliability: % of tool calls that succeed on first attempt.
- Memory Hit Rate: % of tasks resolved using stored entities/context.
- Reversal Rate: refunds or undo actions per 100 tasks.
- Trust/NPS + Evidence Coverage: % steps with verifiable artifacts.
If you can’t measure it, you can’t price it. If you can’t price it, you can’t scale it.
Build it like a founder, not a lab
Execution playbook:
- Start with a wedge
- Pick a workflow with repeatable structure, high pain, and clear receipts.
- Define “done” in one sentence. Everything else is a distraction.
- Design the autonomy gradient
- Level 0: Draft-only.
- Level 1: Act within scopes (budgets, whitelists, time windows).
- Level 2: Act + self-verify with proofs.
- Level 3: Act + verify + self-correct or escalate.
- Treat tools as contracts
- Strong types and idempotent calls.
- Simulate tool failures in CI. Force retry paths.
- Put memory on a leash
- Entities > raw transcripts. Keep a clean graph: people, accounts, tokens, policies.
- Expire aggressively. Re-learn with evidence.
- Enforce determinism where it counts
- Constrained decoding for structure.
- Separate planning (creative) from execution (deterministic).
- Close the loop with data
- Log every step. Rank failures. Distill to a smaller model weekly.
- Reward functions tied to TSR and reversal rate, not word count.
- Price on outcomes
- Per-resolved-task with SLAs, not seats.
- Share savings where provable. Guarantees beat demos.
Infra choices that compound
- Local-first where privacy and latency matter; burst to cloud for heavy lifts.
- Private-cloud or VPC inference for enterprise integrations.
- Secrets and scopes per customer; kill-switch per tool.
- Human-in-the-loop UI built in from day one (approve, edit, undo).
- Observability: traces, screenshots, diffs, and receipts — not just tokens and logs.
AI isn’t the future — it’s the foundation.
Founder takeaways
- Shrink the problem. Own one outcome end-to-end.
- Make compute your advantage: routing, caching, distillation.
- Trust is a product surface: scopes, proofs, reversals.
- Price what you deliver, not what you generate.
- Iterate on failures, not features.
Buildloop reflection
Concierge agents win by being boring in the best way — predictable, provable, and fast. You don’t need to build everything. You need to do one thing better than anyone else, with receipts to prove it.
