Hire AI Agent Developersin 2 weeks
BearPlex AI agent developers build production agentic systems: autonomous workflows with tool use, state management, evaluation harnesses. Specialists in LangGraph, CrewAI, Claude Agent SDK.
What a AI Agent Developer actually does at BearPlex
An AI agent developer at BearPlex specializes in the production architecture of autonomous AI workflows: the systems where an LLM doesn't just answer questions but takes actions, runs multi-step processes, and operates with appropriate autonomy under human oversight. This is its own engineering discipline, distinct from chatbot development. Our agent developers know that LangGraph's explicit state management beats LangChain's AgentExecutor for production work, that tool design discipline (clear descriptions, argument validation, structured error handling) is the #1 driver of agent reliability, that evaluation harnesses must be built BEFORE the agent loop, that human checkpoints on consequential actions are non-negotiable, and that step/cost limits prevent runaway agents from racking up thousands of dollars in API calls. They've shipped autonomous workflows for customer support (multi-step issue resolution), document processing (classify-route-extract-summarize pipelines), DevOps (incident triage and runbook execution), and complex domain-specific agents (legal contract analysis, healthcare prior authorization, financial fraud explanation). They build for production reliability, not demo magic.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Owns the production agent platform for a Fortune 100 logistics company: 47 specialized agents handling distinct workflows, $14M annualized cost savings.
Built the BearPlex internal Claude Agent SDK reference implementation: adopted across 8 active client engagements as the production starting template.
Shipped a multi-agent legal document review system (AmLaw 100 client) that handles M&A due diligence in 3 days vs prior 6-week manual process.
Built BearPlex's internal eval harness for agentic systems: golden trajectory datasets, LLM-as-judge scoring, regression tests across 11 client engagements.
Skills matrix
The capabilities every BearPlex AI Agent Developer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Agent framework expertise (LangGraph, CrewAI, Claude SDK) | Expert | LangGraph · CrewAI · Claude Agent SDK · AutoGen · Custom orchestration |
| Tool design (descriptions, validation, error handling) | Expert | Pydantic · JSON Schema · Structured outputs |
| MCP (Model Context Protocol) integration | Expert | MCP servers · MCP clients · Custom MCP implementations |
| Multi-agent coordination patterns | Advanced | Hierarchical orchestration · Conversational debate · Pipeline patterns |
| Evaluation harnesses (golden trajectories, LLM-as-judge) | Expert | Custom golden datasets · LangSmith · Promptfoo · DeepEval |
| State management & checkpointing | Expert | LangGraph state · Custom persistence · Postgres-based agent state |
| Human-in-the-loop integration | Expert | Approval workflows · Async handoff · Slack/Teams integration |
| Cost & step limit enforcement | Expert | Token budgets · Step counters · Kill switches |
| Observability (tracing, prompt logging, cost tracking) | Expert | LangSmith · Arize · OpenTelemetry · Helicone |
| Prompt injection defense | Advanced | Input sanitization · Output validation · Dual-LLM review |
| RAG integration in agent workflows | Advanced | LangGraph + retrieval · Pinecone · Custom retrieval tools |
| Production debugging & incident response | Expert | Distributed tracing · Trajectory replay · Cost analysis |
How we vet AI agent developers
Technical screen
60-minute call covering production agent experience, tool design philosophy, evaluation strategy. We're looking for engineers who've shipped agents to production AND can explain why theirs failed (they all do at some point).
Live coding
2-hour paired session building a small agentic workflow with constraints (must implement step limits, must handle tool errors, must produce trace logs). We watch for production thinking and instinctive defensive engineering.
Systems design
90-minute design session on a production-realistic agent system (e.g., 'design a customer support agent handling 10K daily conversations with 4 backend tool integrations and human escalation'). We push on capacity planning, observability, failure modes, cost limits.
Reference check + paid trial work
We talk to two prior managers or technical peers. The engineer then completes 1-2 days of paid sample work on a real BearPlex client engagement. Only if all four steps pass do they join the embedded pod.
What clients say
“Most agent developers we've evaluated build for demos. BearPlex's agent developer built for production from day one: observability, cost limits, eval harness all in place before the first agent loop ran.”
“We had built a multi-agent system that worked in development but kept failing in production. BearPlex's developer rewrote it as a single agent with multiple tools and shipped to production reliably in three weeks.”
“The agent BearPlex built for us has been running for 14 months without a single runaway incident. The step and cost limits are why.”
Hiring AI agent developers: questions answered
When your project requires multi-step autonomous workflows with tool use, when you need explicit state management with checkpointing, when human-in-the-loop coordination matters, or when you've hit production reliability issues with your existing agent system. These are the moments where agent engineering becomes its own discipline.
Both, with judgment about which fits the use case. LangGraph for production complexity (explicit state, checkpointing, observability). CrewAI for role-based multi-agent orchestration with simpler API. Claude Agent SDK for Anthropic-first stacks. Custom orchestration when frameworks add overhead without benefit. Framework choice is engineering, not religion.
Most production agents use RAG as one component. Our agent developers handle this fluently. For systems that are primarily RAG-centric with optional agentic patterns, our RAG engineers go deeper. The right specialization depends on which capability dominates the project.
14 days from initial intake to embedded. Day 0 is a 60-minute scoping call. Days 1-7 we match a developer based on your tech stack, domain, and the specific agent challenges. Days 8-14 the developer reads your codebase, sets up local dev, attends standups, and starts shipping by end of week 2.
21 days from start. If the developer isn't a fit during the first 21 days, you don't pay for their time and we replace them at no cost. We've had to invoke this twice in 47 placements.
Most BearPlex agent engagements run 6-12 months. The shortest is a 90-day War Room sprint to ship a production agentic system. Longer engagements expand from initial agent into broader AI orchestration work.
Yes: much of our agent work is in regulated industries. Our developers know the compliance considerations (HIPAA, SOX, attorney-client privilege) and the engineering patterns that make agents safe for high-stakes deployments (mandatory human checkpoints, citation tracking, explicit audit trails).
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async written handoff for the rest.
Three layers: (1) explicit step limits (max iterations per agent run), (2) token budget ceilings (max cost per execution), (3) kill switches (manual pause/abort). Plus observability with cost tracking per agent execution. Our agents have run for 14+ months in production without runaway incidents.
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