AI agent development services from senior AI pod teams
Senior AI pods build agents that call your APIs and tools under guardrails, with approval steps, evaluations and traces, and run them in production.
By the Ryz Labs team · Updated October 2026
Ryz Labs builds AI agents with dedicated pods of senior engineers: agents that plan multi-step work, call your APIs and internal tools, and act only within the permissions and approval steps you define. The pod builds in your cloud and repos, tests every agent against a scenario suite before release, and works on US business hours. Our engineers are the top 1% of the tens of thousands we have interviewed.
What we build
An agent is a model in a loop: it decides which tool to call, reads the result and decides again until the task is done or it has to stop. Useful agents are mostly about the tools, the state and the stopping rules. Typical builds:
- Operations agents. Agents that triage tickets, look up orders or accounts, take a scoped action and leave a structured note for a human.
- Research and analysis agents. Agents that search internal documents and databases, run SQL against a read-only replica and assemble a cited brief.
- Document workflow agents. Intake of forms, contracts or claims: extract fields, check them against systems of record, flag mismatches and route exceptions.
- Customer-facing agents. Support and service agents that can change a booking or reset a setting through your APIs, with handoff to a person.
- Tool layers. Typed tool definitions, function-calling schemas and MCP servers that expose your systems to agents with least-privilege access.
- Multi-agent workflows, when justified. A planner with specialist sub-agents for genuinely separate skills. We start with one agent and split only when evals show it helps.
- Scenario test suites. Recorded tasks with expected tool calls and outcomes, replayed against every prompt, model or tool change.
How an engagement works
- Talk. We walk through the workflow a person does today, step by step, and mark which steps read data and which change it.
- Match. We propose a pod for your stack, usually a tech lead, backend engineers who know your systems' APIs and an AI engineer, with names and a price.
- Join. The pod works in your repos and CI, attends your standups and demos the agent on real tasks every week.
- Grow. Add tools and workflows once the first one is stable, or hand the agent and its test suite to your team.
In week 1, the pod lists the tools the agent needs, gets sandbox credentials and records 30 to 50 real tasks as the first scenarios. By month 1, a read-only version usually runs end to end in a staging environment with traces and scenario scores. By month 3, typical work is adding write actions behind approvals, rolling out to a limited user group and tuning from their transcripts. Your scope and access decide the pace.
The stack our teams work in
| Layer | Tools we use | Notes |
|---|
| Models | Anthropic Claude, OpenAI GPT and reasoning models, AWS Bedrock, Azure OpenAI | Tool-calling reliability varies by model; we measure it on your scenarios. |
| Agent frameworks | LangGraph, OpenAI Agents SDK, Claude Agent SDK, plain code loops | Explicit state graphs for workflows that need checkpoints and resumption. |
| Tools and protocols | Function calling with JSON Schema, Model Context Protocol, REST and GraphQL clients | Each tool gets a typed schema, input validation and its own credentials. |
| State and memory | Postgres, Redis, pgvector | Durable task state so a crashed run can resume, not restart. |
| Tracing and evals | Langfuse, LangSmith, OpenTelemetry, scenario replay in CI | Every model call and tool call is traced with inputs and outputs. |
| Runtime | AWS Lambda or ECS, Azure Container Apps, Kubernetes, queues such as SQS | Long tasks run as background jobs with timeouts. |
How we keep agents safe and reliable
Agents fail differently from chatbots, because a wrong step can change data. The failure modes a senior pod designs against:
- Prompt injection through tool results. A web page, email or ticket can contain instructions aimed at the agent. We treat tool output as untrusted data, keep sensitive tools out of reach of agents that read external content, and require approval for consequential writes.
- Over-broad permissions. Each tool gets the narrowest credential that works: read-only where possible, scoped to the user the agent acts for, never a shared admin key.
- Loops and runaway cost. Hard caps on steps, tokens and wall-clock time per task, with a clean failure message when a cap is hit.
- Duplicate actions. Retries happen. Write tools use idempotency keys, so a retried "issue refund" call does not issue two refunds.
- Wrong tool, wrong arguments. Tool descriptions are written and tested like an API contract, arguments are validated before execution, and scenario tests check the exact calls made.
- Silent drift. A model upgrade can change tool-calling behavior. Versions are pinned and the scenario suite reruns before any switch.
- No way to explain what happened. Every run keeps a trace of model calls, tool calls and approvals, so your team can review any decision the agent made.
Our pods have shipped agents like this to production, including an AI driver-support agent that covers about 218,000 driver calls a year in three languages, at about a 95% lower cost per call according to the live case study, and a 24/7 AI real-estate agent. See the case studies.
Team shapes and cost
Ryz engineers typically cost $7,000 to $15,000 per engineer per month. Mid-level (comparable to Amazon L5) is $7,000 to $10,000, senior (comparable to Amazon L6) is $10,000 to $15,000, and leads start at $15,000.
- Pilot pod for one workflow: a tech lead plus 2 senior engineers. 1 × $15,000+ plus 2 × $10,000 to $15,000 = about $35,000 to $45,000+ per month.
- Production pod for several agents: about 7 senior engineers, including a tech lead, an ML engineer and backend engineers. 1 × $15,000+ plus 6 × $10,000 to $15,000 = about $75,000 to $105,000+ per month.
- Agent specialist on your team: 1 senior AI agent engineer at $10,000 to $15,000 per month.
Project cost is team size × duration × monthly rate. Before work starts, you get a scoped plan, a price and the names of the people.
Dedicated team or staff augmentation?
An AI pod team fits when the agent touches several systems and someone needs to own tools, evals, security review and rollout together. If your platform team already runs model infrastructure and you need agent expertise, hire AI agent developers or LangChain developers through staff augmentation; they work on your team, reporting to your leads.
When Ryz isn't the right fit
If you want an off-the-shelf agent platform with a license and a vendor roadmap, buy from a platform vendor. If you need follow-the-sun coverage or engineers in European or Asian time zones, a global network fits better. If you want a quick freelance prototype with no conversation first, use a marketplace.
Related
FAQ
What is the difference between an AI agent and a chatbot?
A chatbot answers. An agent decides which tools to call and takes actions, such as updating a record or filing a ticket, over several steps. That makes agents more useful and riskier, so they need scoped permissions, approvals and scenario tests.
Which agent framework do you use?
Whatever fits your workflow and team. LangGraph suits workflows that need explicit state and checkpoints, provider SDKs suit simpler loops, and plain code is often easiest to maintain. We avoid adding a framework your team will not want to own.
How much does AI agent development cost?
Ryz engineers typically cost $7,000 to $15,000 per engineer per month, leads from $15,000. A three-person pilot pod is about $35,000 to $45,000+ per month, and total cost is team size × duration × monthly rate.
How quickly can an agent team start?
After the scoping call we propose a team with names. Most of the timeline depends on scope and on how quickly your team can provide sandbox access to the systems the agent will use.
Can agents take actions without a human approving them?
They can, for low-risk actions you choose. We usually launch with approvals on every write, measure how often people override the agent, and remove approvals step by step where the data supports it.
Questions we didn't answer? Email info@ryzlabs.com.