Hiring an AI Agent Development Team: What to Vet
Hire an AI agent development team with confidence by vetting vendors on their eval and observability practice, not just demos of AI agents working in ideal conditions.
AI agents are the most demo-friendly technology we've seen in years. A vendor can show a compelling five-minute walkthrough of an agent that researches leads, drafts emails, and logs CRM entries, and the audience is hooked. What the demo never shows is what happens on query 200, when the tool-call sequence hits an unexpected API response, the agent loops, and your bill triples without producing a single usable output.
The gap between a happy-path demo and a production-grade AI agent system is enormous. Most teams discover this only after signing a contract. The vendors who bridge that gap successfully are the ones who have built evaluation pipelines, instrumented their agents with proper observability, and can show you failure cases as readily as success cases.
We've evaluated dozens of vendors and built AI agent systems at Laxaar across customer support, data enrichment, and internal automation use cases. The pattern is consistent: the question that separates serious teams from demo-builders is not "can you show me it working?" but "can you show me how you know when it's not working?"
What you'll learn
- Why demos are the wrong evaluation criteria
- What an eval suite actually looks like
- Observability requirements for production agents
- Tool design and failure-mode thinking
- How to assess agent architecture decisions
- Comparing vendor maturity on a scoring rubric
- Red flags in an agent development proposal
- Frequently Asked Questions
Why demos are the wrong evaluation criteria
A demo is a rehearsal. The vendor has run it before, they know which edge cases to avoid, and they've tuned the prompt to work on the three or four examples they're going to show you. Demos are useful for understanding the intended product, but they're useless for evaluating whether the team can maintain that product over time.
Production AI agents fail in ways that are invisible to casual observation: they produce plausible-sounding but wrong outputs, they call the right tool with subtly wrong parameters, they handle 95% of cases correctly and quietly mishandle the rest. The only way to catch these failures is through systematic evaluation. The only way to know a vendor has that capability is to ask them to show you their eval setup, not their demo.
There's also a deeper issue. Anyone can wrap a model in a few API calls and ship something that works on easy inputs. The real engineering skill in AI agent development is handling the long tail: ambiguous inputs, partial tool failures, context that doesn't fit the expected schema, rate limits that interrupt a multi-step workflow mid-run. Teams that haven't encountered these problems (or haven't built systems to surface them) are selling you a prototype, not a production system.
What an eval suite actually looks like
An eval suite is a structured set of test cases with expected outputs, graders, and pass/fail thresholds. For AI agents, it's more complex than traditional unit tests because agent outputs are often non-deterministic and multi-step.
A mature eval suite for an AI agent includes at minimum:
Input coverage. Test cases that span the happy path, edge cases (malformed inputs, missing fields, ambiguous queries), and known failure modes from production. If a vendor's eval set contains only clean, well-formed inputs, they've only tested the easy version of the problem.
Output graders. Automated checks on agent outputs, which can be rule-based (did the agent call the right tool?), model-based (did a grader model rate the output above a threshold?), or human-labeled (a held-out set with human judgments). The best teams use a combination. Relying solely on model-graded evals is a known weakness: the grader model shares the same failure modes as the generator.
Regression tracking. Evals run on every meaningful change to prompt, model version, or tool schema. A vendor who reruns evals only before major releases is treating AI like traditional software, where behavior is deterministic. Model providers ship updates, prompts degrade, and tool APIs change, so regression tests need to be continuous.
Ask the vendor point-blank: "Can you walk me through your eval suite for your last shipped agent?" If they describe a process, great. If they say "we do extensive testing," push for specifics. Vague answers to concrete questions are a reliable signal.
Eval checklist questions to ask any vendor:
1. How many test cases are in your eval suite for a typical agent?
2. What proportion are edge cases vs. happy-path inputs?
3. How do you grade outputs — rule-based, model-graded, or human?
4. How often do evals run — on every PR, on release, or ad hoc?
5. Can you show us a recent eval run and the failure cases it caught?
Observability requirements for production agents
Observability for AI agents is not the same as application logging. Standard logs capture what your code did. Agent observability captures why the agent made each decision: which tools it called, in what order, with what inputs and outputs, and what the model's reasoning trace looked like at each step.
Without this level of instrumentation, debugging a production incident is nearly impossible. You know the agent produced a wrong result, but you can't trace back through the tool calls to find where the reasoning went wrong. Every incident becomes a guessing exercise.
A production-ready AI agent system needs four observability layers:
Trace-level spans. Each agent run captured as a trace with child spans for every tool call, model inference, and decision point. Tools like Langfuse, Arize Phoenix, and Helicone provide this. Ask which one the vendor uses and whether you'll have access to the traces, not just aggregated dashboards.
Latency and cost tracking. Agent costs can be unpredictable. A single run that loops unexpectedly can cost 50 times what a normal run costs. Token counts, tool-call counts, and wall-clock latency per run should be tracked per task type, not just averaged across all traffic.
Error categorization. Failures should be tagged by type: tool call failure, model refusal, context overflow, schema validation error, timeout. Untagged errors are nearly impossible to prioritize or fix systematically.
Anomaly alerting. If an agent's average tool-call count per run doubles overnight, that's a signal worth investigating before users notice. Threshold alerts on behavioral metrics (not just uptime) are a mark of a mature team.
| Observability layer | Basic | Good | Production-ready |
|---|---|---|---|
| Trace capture | None | Single-level logs | Full span tree per run |
| Cost tracking | None | Total daily spend | Per-run, per-task breakdown |
| Error tagging | None | Error vs. success | Categorized by failure type |
| Alerting | None | Uptime only | Behavioral anomaly alerts |
If a vendor can't show you what their observability stack looks like for a live deployment, that's a significant gap. Not a minor one.
Tool design and failure-mode thinking
The tools an agent can call are the boundary of what it can do and the source of most of its production failures. Tool design is both an engineering and a product decision, and vendors who treat it as an afterthought produce agents that fail in frustrating ways.
Good tool design starts with narrow schemas that reduce the surface area for parameter errors. Docstrings matter too: the model reads tool descriptions the same way a developer reads an API doc, so ambiguity causes wrong calls. Beyond that, tools need explicit error responses the agent can reason about and retry from, and idempotency where the operation permits it so a double-call doesn't corrupt state.
Ask the vendor to walk you through a tool schema they've designed. Listen for whether they discuss failure modes. A team that's shipped production agents will immediately bring up: what happens if this tool returns a 429, what the agent does if the tool returns a result in an unexpected format, and whether the tool response can be trusted or needs validation before the agent acts on it.
This is the opinionated take worth holding: tool reliability is a naming and schema problem before it's a model problem. We've seen agents at Laxaar switch from consistently wrong tool selections to consistently correct ones purely by improving tool descriptions. Same model, same prompt structure, just better-named tools with more precise docstrings.
How to assess agent architecture decisions
Architecture for AI agent systems covers choices that have long-lived consequences: single-agent vs. multi-agent, synchronous vs. asynchronous execution, stateless vs. stateful memory, and how context is managed across multi-step tasks.
Each of these has real trade-offs, and the right answer depends on your use case. What you want from a vendor is evidence that they've reasoned about these trade-offs for your specific problem, not that they've defaulted to a template.
A few questions that surface architectural depth:
"How do you handle context that exceeds the model's context window in a long-running task?" Good answers involve chunking, summarization, or retrieving context on demand. "We use a large context model" is not an architecture.
"What's your approach to agent state between steps?" Good answers specify where state is stored (database, vector store, in-memory), what the recovery path looks like if a step fails mid-run, and how state is scoped to avoid cross-contamination between concurrent runs.
"When would you recommend a single-agent system over a multi-agent one?" The honest answer is "almost always for a first deployment." Multi-agent systems add coordination complexity, failure surfaces, and debugging difficulty that rarely pay off until the task genuinely requires parallelism. A vendor who defaults to multi-agent without justification is chasing architectural sophistication, not solving your problem.
For teams considering our AI agent development services, we ask and answer these questions in our discovery process before any architecture is proposed.
Comparing vendor maturity on a scoring rubric
Use this rubric to score vendors before and after discovery calls. Score each dimension 1-5, then weight by the column.
| Dimension | Weight | Description of a "5" score |
|---|---|---|
| Eval practice | 35% | Documented suite, edge-case coverage, continuous regression runs |
| Observability stack | 25% | Full trace capture, cost-per-run tracking, anomaly alerts |
| Tool design quality | 20% | Narrow schemas, explicit failure handling, idempotency where relevant |
| Architecture reasoning | 15% | Trade-off aware, use-case specific, not template-driven |
| Production references | 5% | Live agents in production they can speak to directly |
The 35% weight on eval practice is deliberate and higher than most buyers assign. Evaluation is the foundation everything else rests on. A vendor with beautiful architecture but no evals has no reliable way to know if their system is degrading after a model update, a prompt change, or a tool schema revision.
Production references get only 5% not because they don't matter, but because they're hard to verify and easy to curate. A single honest conversation about a difficult production incident (what went wrong, how it was diagnosed, what the fix was) tells you more than ten polished case studies.
If you want to see how the Laxaar team approaches this evaluation for clients, our portfolio includes AI agent deployments across customer support, internal tooling, and data enrichment workflows.
Red flags in an agent development proposal
Watch for these in written proposals and discovery calls:
"We'll tune the prompts until it works." This is not an engineering plan. Prompt tuning without evals is iterating blind. You don't know if each change is an improvement or a regression on cases you haven't tested yet.
No mention of failure modes. A well-written proposal for an AI agent system will explicitly name the edge cases and failure scenarios the team has considered. If the proposal reads like a feature list with no failure acknowledgment, the team hasn't thought carefully about production.
Observability described as "we'll add logging." Logging is not agent observability. If a vendor describes their post-deployment monitoring plan as adding some console logs or connecting to a basic APM tool, they haven't shipped a production AI agent system before.
Fixed-price engagement for a novel agent problem. Fixed-price contracts work when requirements are stable. AI agent development involves significant uncertainty in the early stages: what the model can reliably do, where tools will need redesign, what the eval failure rate is at launch. A vendor who quotes fixed-price for a first-of-kind agent deployment is either not accounting for that uncertainty or planning to cut corners when the uncertainty surfaces.
Demo-only references. If every reference a vendor offers shows you a demo rather than connecting you with an actual production user, that tells you something. Ask specifically: "Can we speak to someone running this in production today?"
To explore how we structure AI automation services or review how we handle AI development engagements end-to-end, our services page has the full picture.
Frequently Asked Questions
How do we evaluate an AI agent vendor if we don't have an AI engineer on our team?
Focus on process evidence rather than technical depth. Ask to see an eval run. Even if you can't read the code, you can assess whether the vendor has a systematic process or a manual one. Ask for a reference conversation with a production user, not a demo. And consider hiring a freelance AI engineer for a single day of vendor review; the cost is negligible relative to a six-month engagement gone wrong.
What's a reasonable timeline for a production AI agent project?
For a focused, single-task agent with clear tool boundaries, eight to twelve weeks to a production-ready system is realistic. That includes discovery, tool design, initial build, eval suite development, and observability setup. Timelines shorter than six weeks for anything non-trivial usually mean the eval and observability work is being skipped, not that the team is faster. Be skeptical of aggressive timelines from vendors who haven't done a scoping session.
Should we build our AI agents in-house or hire a specialist team?
It depends on whether your competitive advantage is the agent itself or what the agent enables. If you're building a product where the agent is the core differentiation (and you intend to iterate on it continuously), building in-house makes sense once you have the right people. If the agent is infrastructure that supports another product, bringing in a specialist team to build and hand off is usually faster and cheaper. The Laxaar team works both models: full build-and-hand-off engagements and embedded partnerships where we work alongside your team.
How much does it cost to hire an AI agent development team?
Costs vary widely depending on agent complexity, tool integrations required, and the maturity of your existing infrastructure. A straightforward single-task agent with two or three tool integrations might run $30k to $60k for a full build-to-production engagement. A multi-agent system with custom eval infrastructure and ongoing support contracts can run significantly higher. Be wary of quotes below $20k for anything described as "production-ready." That budget won't cover proper eval and observability work.
What should a vendor deliver at the end of an AI agent engagement?
At minimum: the agent code with documented tool schemas, the eval suite with instructions for running and extending it, observability setup with access credentials, a runbook for common failure modes and recovery steps, and a handoff session covering architecture decisions and known limitations. If a vendor's definition of "done" is a deployed agent that works in demos, negotiate the eval suite and runbook into the contract deliverables before work starts.
The market for AI agent development teams is full of capable prompt engineers and thin on teams who've actually maintained agents in production. The difference shows up not in demos but in whether a vendor can hand you an eval suite, open a trace for a failed run, and explain exactly what went wrong. That's the bar worth holding.
If you're scoping an AI agent project and want to see how the Laxaar team approaches eval-first development and production observability, talk to us or request a quote. We're happy to show you our process before you decide.
Working on something like this?
Get a fixed scope, timeline, and price within one business day — no obligation.
