AI agents, shipped to production not pitched in slides.
Most AI agent demos look incredible and never ship. We build agents that go to production: lead scoring, support triage, outbound personalization, sales call summarization. Each one runs as a serverless function with monitoring, guardrails, and version-controlled code your team owns.
Agents that actually run in production.
Four real AI agent builds, four real outcomes. Each card links to the full case study with the use case, technical scope, and measured impact.
62% tier-1 resolved without a human
Anchor's support triage agent reads tickets, classifies them, and resolves common issues directly. Human handoff for edge cases.
AI lead scoring tied to deal weight
Promptly's predictive scoring agent reads product usage signals and updates lead score every 15 minutes.
Outbound reply rate up 4.1x
AdLib's outbound agent personalizes opening lines using public signals (LinkedIn posts, recent funding, hiring).
Agent summarizes 12-system patient data
Anchor's care coordination agent pulls patient context from 12 systems into one timeline for clinical decision-making.
When AI agents fit, and when they truly don't.
We've shipped agents that worked, and we've told customers no when LLM-powered automation wasn't the right answer. Below is the honest read.
Right fit when
- Your use case has structured inputs and structured outputs. LLMs work best when the task is well-defined.
- You can tolerate occasional misclassification with human-in-the-loop fallback.
- The cost per call (typically $0.01 to $0.05) is justified by the time saved or revenue gained.
- You want to own the code and run the agent on your own infra (AWS Lambda, GCP Cloud Functions, Vercel).
- You have a clear evaluation set so we can measure agent quality before going to production.
Wrong fit when
- Your use case requires perfect accuracy with zero tolerance for hallucination (regulated medical, legal advice, financial decisions).
- You don't have an evaluation set or a way to measure quality. Pure vibes-based AI evaluation leads to bad agents in production.
- You're chasing a buzzword from leadership without a real business case behind it.
- Your data is too thin or too unstructured for an LLM to find signal. AI doesn't fix bad data.
How we build agents that hold up.
Production AI agents need more than a prompt. Below is the structure we use across every build.
Triggers + structured data
Webhook, scheduled job, event, or chat trigger. Inputs are validated and shaped before reaching the LLM. No raw user input goes straight to a prompt.
Prompt + guardrails
Carefully versioned prompts. Token limits enforced. Output schema validated with retry logic. Eval set runs in CI on every prompt change. Hallucination protections built in.
Action + monitoring
Output triggers a HubSpot workflow, writes to a database, sends a Slack message, or updates a CRM record. Every action logged. Errors page Slack. Daily quality reports.
From kickoff to agent in production.
Six steps. Same approach used on every agent above. Built to ship reliable agents your team can trust.
Use case
Two sessions with stakeholders. Use case clarity, structured inputs and outputs, success metrics, evaluation criteria. Output: agent specification with measured-impact targets.
Eval set
We collect 50 to 200 representative examples with expected outputs. This is the test set we will use to measure quality before and after launch. No agent ships without an eval set.
Build
Prompt engineering, schema design, retry logic, error handling. Iterated against the eval set. Senior engineers ship in TypeScript or Python with tests covering edge cases.
Deploy
Stage in non-prod with shadow-mode running. Compare agent decisions against human decisions for 1 to 2 weeks. Rollout when shadow-mode quality matches or exceeds human baseline.
Monitor
Daily quality reports against eval set. Slack alerts for output schema violations. Hallucination detection. Cost monitoring. Drift detection on prompt changes.
Hand off
Code in your repo. Documentation covering prompts, eval methodology, monitoring, escalation paths. Your team owns it. Optional retainer for ongoing tuning and net-new agents.
Inside an AI agent build.
Real deliverables, not capability bullets. Below is the typical scope for a production agent, fixed-fee from $24,500 per agent.
Spec + Eval
- ·Agent specification with measured-impact targets
- ·50 to 200 example eval set with expected outputs
- ·Architecture document covering triggers, LLM call, outputs
- ·Cost estimate per call and at expected production volume
- ·Sign-off gate before coding begins
Build
- ·Versioned prompts with structured output schema
- ·Retry logic, error handling, hallucination protections
- ·TypeScript or Python serverless function
- ·Unit tests covering happy path and edge cases
- ·Eval suite running in CI on every prompt change
- ·Code review against your team's standards
Deploy
- ·Shadow-mode rollout for 1 to 2 weeks
- ·Quality comparison against human baseline
- ·Staged rollout with feature flags
- ·Slack alerts wired to your incident channel
- ·Daily quality reports against eval set
Hand off
- ·Code committed to your repo
- ·Architecture and prompt-engineering documentation
- ·Operational runbook with common-failure paths
- ·Suggested optimization roadmap for months 4-12
Per-agent. Complexity-aware.
Light agents (single-task classification or summarization): $14,500. Standard agents (multi-step, tool use, structured output): $24,500. Enterprise agents (multi-agent workflows, custom evaluation, sustained monitoring): $48,000+. Ongoing tuning retainers from $5,000 monthly.
Things people ask.
Which LLM providers do you use?+
Claude (Anthropic) for most production agents. GPT-4 family (OpenAI) for specific use cases. Gemini (Google) where Google ecosystem is the right fit. We're model-agnostic and pick what works best per use case. Self-hosted open-source models (Llama, Mistral) for customers with strict data residency requirements.
How do you handle hallucination?+
Structured output schemas with validation. Retrieval-augmented generation when the agent needs grounded facts. Eval suites that catch hallucination patterns. Confidence thresholds with human-in-the-loop fallback. We measure hallucination rate before and after deployment as a tracked metric.
What's the cost per call?+
Typically $0.01 to $0.05 per call depending on prompt length and model. Standard agents at production volume cost $200 to $2,000 monthly in LLM API spend. Enterprise multi-step agents can run $5K to $25K monthly. We size and project costs as part of the build.
Can you integrate agents with HubSpot?+
Yes. Most of our agents integrate with HubSpot as the system of record. Triggers come from HubSpot workflows. Outputs write back to deal records, contact properties, ticket fields, or trigger downstream automations. Native to our practice.
Where does the agent run?+
Your infra. AWS Lambda, GCP Cloud Functions, Vercel Edge, Cloudflare Workers, or your own Kubernetes cluster. We don't host agents on our infra (vendor-lock and trust concerns). Code is committed to your repo.
Do you do AI strategy or only build?+
Both. About 30% of our AI engagements start with a strategy phase: where to invest, what use cases to prioritize, what infra to stand up, what governance model to adopt. The other 70% start with a specific use case in mind and we ship that agent.
What about data privacy and compliance?+
We work with HIPAA-aware setups, GDPR data residency requirements, SOC 2 controls. For sensitive data, we typically self-host open-source models or use Anthropic's enterprise plan with zero data retention. We design for compliance from day one.
How do we get started?+
Book a 30-minute strategy call. We'll cover your use case, data, success metrics, and the right approach. Proposal within 48 hours if we're a fit.
How INSIDEA works
