The AI agent agencythat ships, scores, closes, triages, loopsagents that act, not chatbots.
An AI agent isn't one more ChatGPT assistant on top of your stack. It's an autonomous operator that creates the lead, scores the deal, closes the ticket, sends the follow-up. We deploy agents that do the work — not chatbots that answer "how can I help you".
ActiveCampaign
Adalo
AdCreative.ai
Ahref
Airtable
Allo (The Mobile First Company)
Anthropic
Apify
Apollo.io
Attio
Base44
Baserow
Brevo
Bright Data
Browse AI
Bubble
CaptainData
ChatGPT
Claude
Claude Code
Claude Cowork
Clay
Clickup
Cursor
Deepseek
Dust
ElevenLabs
Fillout
Flutterflow
Folk CRM
Freepik Spaces
Gamma
Gemini
Glide
Grok
HiggsfieldAn AI agent that actually ships stands on 4 pillars.
Most "AI agent" pilots die between the demo and the rollout for the same handful of reasons: vague use case, no tool integration, no eval, no monitoring. The stack we deploy in 2026 closes all four gaps from day one.
- Design
Operational use-case selection
We don't deploy an agent because the meeting was exciting. We map your processes, score them on volume / variability / value, and ship an agent only where it beats the cost of doing the same task by hand. Most teams have 3 to 5 obvious targets they never noticed.
How we pick use cases - Orchestration
Tool calling + MCP
An agent that can't act is a fancy chatbot. We wire tool calling (function calling, retrieval, code interpreter) plus Model Context Protocol servers so the agent reads from and writes to your CRM, your knowledge base, your tickets, your inbox. Concrete actions, not prose.
See the agent stack - Deployment
Inside your existing stack
Agents sit inside HubSpot, Salesforce, Pipedrive, Zendesk, Slack, Make, n8n — wherever the work already happens. No standalone interface no one logs into, no extra SaaS subscription to babysit. The team works where they already work.
See the integrations - Monitoring
Evals + guardrails
Every agent ships with a test suite (eval), output filters (cost ceiling, response length, refusal handling) and a logging pipeline you can audit. If an agent starts costing $4/conversation or hallucinating tool calls, you catch it the same week, not the month after.
What we measure
What an agent in production actually moves.
- −65%Time spent on the task
Across the 3-5 use cases we deploy on a typical mission (CRM hygiene, ticket triage, RFP scoring, content drafting, scheduling), the agent crushes the cycle time. The team handles only the edge cases.
- $0.06Avg cost per agent run
On a well-prompted Claude or GPT-4o agent with retrieval and 2-3 tool calls. We benchmark every deploy. If unit cost drifts above $0.20 the eval pipeline alerts us before it shows up on the invoice.
- ×7Tasks closed per FTE
On the cohorts we've shipped — sales ops, support L1, content production. The team doesn't disappear, the volume that flows through them does. The bottleneck moves from execution to decisions.
Our 4-step build, from process to production.
We treat every agent as a small software product, not a prompt-engineering experiment. Same shape regardless of whether the agent lives in HubSpot, Zendesk, Slack or a custom internal tool.
- Discover · score every candidate process on volume, variability and value
- Design · system prompt, tool schema, guardrails, eval set, all written before any code
- Build · agent wired in n8n / Make / native SDK with the right model + retrieval
- Deploy · agent embedded in your CRM, Slack, Zendesk — wherever the work happens
Agents that do work, not chatbots that answer.
A chatbot answers. An agent reads the goal, fetches the data, picks the tool, executes the action, observes the result, decides the next step. The line is concrete. Every agent we ship can be measured by the actions it performs in your systems — not by how nicely it talks.
- Agents do work. They create a lead, score a deal, close a ticket, send an email.
- We pick the model (Claude, GPT-4o, open-weights) per task, not per fashion
- MCP servers expose your tools cleanly — agents never touch a brittle integration
- Every action logged, every prompt versioned, every cost line attributable
We score your candidate processes, you leave with a plan.
Before quoting anything, we spend 60 minutes mapping the processes that deserve an agent and ranking them on volume, variability and value. You walk away with a ranked candidate list and the first agent's design draft — yours to ship in-house or with us. Zero pitch, just an outside look at what to automate first.
- Use-case scoring on every repetitive process you flag
- Top 3 candidates with rough cost-to-build and expected ROI
- Design draft for the first agent (prompt, tools, eval set)
- Honest take on where an agent would be a worse-than-status-quo solution
How we run an AI agent engagement.
Five steps, in order, no skip. We don't open an editor before the design doc is signed, we don't deploy without an eval pass, and we don't bill a retainer before the first agent is running in production. Every step has a DOD and you approve before we move to the next.
- Step 1 · Process audit
Audit which processes deserve an agent (and which don't)
We sit down with the team that runs the work — sales ops, support, ops, content, recruiting — and score every repetitive process on three axes: volume (how often it runs), variability (how much the input shape changes), and value (how much time or money it costs you today). Most teams have 3 to 5 obvious agent candidates they were too close to spot. We also flag the processes where an agent would be a worse-than-status-quo solution. You walk away with a ranked candidate list and three quick wins to ship inside 30 days.
- Step 2 · Agent design
Design the agent before you build it
System prompt drafted in plain English. Tool schema defined: which read-only and write actions the agent is allowed to perform, with explicit parameter shapes. Guardrails listed: max tokens per call, max tool calls per session, refusal patterns, escalation paths to human operators. Eval set built: 30 to 80 representative inputs with expected outputs the agent has to clear before promotion. None of this is code yet. The doc is signed off by an operator on your side before we open an editor.
- Step 3 · Build the agent
Build the agent on the right model and runtime
We pick the runtime that fits the constraint: Claude Agent SDK or OpenAI Agent Builder when latency matters and Anthropic / OpenAI native tools fit the bill; n8n or Make when the agent has to chain through 5+ services your team already knows; LangChain or a custom Python service when the agent needs deep retrieval or fine-tuned routing. Model picked per task: Claude Sonnet for reasoning, Claude Haiku for high-volume cheap loops, GPT-4o for vision-heavy work, Mistral or local Llama for sensitive data. Cost benchmarked per run from day one.
- Step 4 · Deploy in-place
Deploy the agent inside the tools your team already lives in
Agents don't deserve their own SaaS interface. Sales agents live inside the CRM as a slash command or a sidebar panel (HubSpot, Pipedrive, Salesforce, Attio, Folk). Support agents reply directly inside Zendesk, Intercom or Slack threads. Ops agents trigger on a calendar event, a Stripe webhook or a Slack message. Content agents push drafts to Notion or Webflow CMS. The team doesn't learn a new tool, they get a faster version of the one they already use.
- Step 5 · Eval + monitoring
Run the eval suite, watch the cost, iterate every month
Every agent ships with the eval set built in step 2, run on a cadence and on every prompt change. Costs tracked per agent per day (Helicone, Langfuse, custom logging into Supabase / BigQuery). Refusal rate, hallucinated tool calls, response length distribution, latency, fallback-to-human rate — all on a dashboard you check whenever you want. Monthly review with us: what to extend, what to retire, what to retrain. The agent gets better over the months, it doesn't decay.
The same stack, across multiple client agents.
The frames below are pulled from real monthly review calls with clients running agents in production — eval pass rate refresh, cost-per-run trends, model migration plans, the queue of new use cases to extend the agent fleet. Same operational rigor, different industries, all in B2B services, SaaS and ops. Our Trustpilot reviews come from the operators we work with.
- Monthly eval review with every client running 1+ agents in prod
- Cost-per-run dashboard updated in real time, no quarterly slide deck
- An eval regression triggers a rollback before the next deploy
- Trustpilot reviews come from the operators using the agents, not from marketing
The 10 questions we get asked on every call.
What's the difference between an AI agent and a ChatGPT-style assistant?
A ChatGPT assistant answers a question and stops. An AI agent reads the goal, picks the tools, executes the actions, observes the result, decides the next step, and loops until the task is done. Practically: an assistant writes you a draft email; an agent reads the incoming ticket, fetches the order in your system, drafts the reply, attaches the right policy document, sends it, and logs the touch in your CRM — all without you in the loop. The agent has tool access (function calling, retrieval, code) and a feedback loop. That's the line.How much does an AI agent agency cost in 2026?
Depends on scope and ambition. A focused mission (one agent, one process, audit + design + build + deploy) runs $8,000 to $25,000 depending on the integrations required. A monthly retainer covering 3 to 8 agents in production (extensions, evals, cost monitoring, model migration) starts around $4,000-$8,000/month. Watch out for agencies that charge by 'AI hours' or pitch a vague 6-month 'AI transformation' — that's consulting fluff. Our approach: a free audit first, then a price per agent shipped, not per hour talked.What's the difference between Claude, GPT-4o, Mistral and open-weights for agents?
Each model has a different strength. Claude Sonnet 4.x leads on long-context reasoning, careful tool use and refusing weird prompts cleanly. GPT-4o is faster on multimodal work (vision, voice) and has the most mature function-calling tooling. Mistral Large is competitive on French language and EU data residency. Open-weights (Llama 3.x, DeepSeek, Qwen) work when you need to keep data on-premises or your unit cost ceiling is sub-$0.01. We don't marry one model — we pick per use case and we re-benchmark every 6 months when a new generation ships.How long does it take to ship a first AI agent in production?
Honest answer: 3 to 6 weeks for a first agent on a well-scoped use case. Week 1 audit + use-case scoring. Week 2-3 design (system prompt, tool schema, eval set, guardrails). Week 3-4 build + integration. Week 5-6 internal beta, eval pass, prod deploy with a kill switch. If an agency promises an agent in production in 1 week, they're skipping evals — fine for a demo, dangerous in front of paying users.Does an AI agent replace the team or augment it?
Augments. Every agent we ship has an escalation path back to a human operator — for the edge cases, the angry customers, the high-value deals. What changes: the team stops doing the 80% of repetitive work the agent crushes and refocuses on the 20% that actually needs judgment. We see this on every cohort: sales ops moves from 'cleaning CRM data' to 'building the playbook', support L1 moves from 'copy-paste replies' to 'fixing the root cause that generated the ticket'.What's MCP and why does it matter for AI agents?
MCP (Model Context Protocol) is the open standard Anthropic shipped to let LLMs talk to tools, files and databases in a uniform way. Before MCP, every agent had a bespoke integration with every system you cared about (the CRM, the wiki, the file storage, the ticketing tool) and a model update could break all of them. With MCP, the agent talks to an MCP server, and the server is the only place you wire integrations. Cleaner, more portable, easier to swap models. We default to MCP for any new agent that needs more than 2-3 tools.Can we run AI agents on our own infrastructure for sensitive data?
Yes. We deploy agents on three patterns depending on your constraint: (1) Anthropic / OpenAI API with zero-data-retention and EU residency enabled — fine for 90% of B2B teams. (2) Azure OpenAI, Bedrock, or Vertex AI on your own cloud account — better for regulated industries with existing cloud commits. (3) On-premise or on-VPC inference with Llama 3.x / DeepSeek / Qwen via vLLM or TGI — for finance, defense, healthcare and the 1% of cases where data legally can't leave your perimeter. We size cost and latency tradeoffs honestly before recommending one.Which CRM and tools do you wire AI agents to?
Tool-agnostic. We've shipped agents wired to HubSpot, Pipedrive, Salesforce, Attio, Folk, Airtable, Notion, Zendesk, Intercom, Slack, Gmail, Outlook, Stripe, Linear, GitHub, Webflow, Make, n8n, and custom internal systems via REST APIs or Postgres. The wiring lives behind an MCP server or a no-code workflow (Make / n8n) when the team will need to extend it without code. If you have a documented API and webhooks, we can wire an agent to it.How do you prevent agents from hallucinating or going off-script?
Four layers. (1) Tool schemas with strict JSON output validation — the agent literally can't call a tool with malformed arguments. (2) Eval set run on every prompt change with 30-80 representative cases, the agent has to score above a threshold before going to prod. (3) Output filters: max tokens, max tool calls, max cost per session, refusal patterns for off-topic inputs. (4) Logging into Helicone or Langfuse so every call is reviewable, with a weekly sample audited by an operator on your side. Hallucinations don't disappear, they get caught and fixed.How long do we commit for?
Three formats. (1) Audit only: flat fee, 2 weeks, deliverable is the ranked use-case list and the design doc for the first agent. (2) Build sprint: 4 to 8 weeks per agent shipped, fixed scope, fixed price. (3) Ongoing retainer: 6-month minimum for teams running 3+ agents in production who want continuous eval, model migration and use-case extension. No forced annual contract, no convoluted exit clauses. If we don't ship, you stop.
Stop pitching the agent. Ship it.
A 60-minute audit, three candidate processes scored, one agent designed. If your team should build it in-house, we'll say so and hand you the design. If we're a better fit, we ship in 4 to 8 weeks.