What to Look for in an AI Agent Development Partner

How to pick the right AI agent development partner (a practical guide)

Read this and you'll know what questions to ask, what technical capabilities truly matter, how to spot hidden risks, and how to choose a cost and delivery model that matches your tolerance for risk and speed.

Start by clarifying the agent you actually need

Before you talk to vendors, decide what "success" looks like: which tasks the agent must complete, what data it needs, and how it should integrate with your systems. That scope drives whether you need a narrow automation, an LLM-based assistant, or a hybrid system that combines retrieval, tool use, and structured workflows.

Technical expertise that actually matters

Many vendors emphasize models, but the system around the model often determines success.

• Data engineering and pipelines: Reliable ETL, secure APIs, and real-time feeds are the backbone of an agent. If your data is late, inconsistent, or siloed, agent quality will suffer regardless of which model you use.

• Integration and ops: Look for experience with your stack (cloud provider, IAM, API gateways, event buses). Real-time or near-real-time agents typically rely on streaming platforms and event-driven architectures.

• Model types and relevance: Confirm the partner can work with the model family you need (retrieval-augmented generation, fine-tuned LLMs, policy models, or classical ML components).

AI model sourcing and licensing transparency

Ask whether the partner builds models in-house, fine-tunes third-party foundation models, or primarily composes off-the-shelf models. Licensing and usage restrictions differ by provider and model—using a foundation model without the right license can create legal and commercial risk.

Proven track record and domain experience

Past success in projects similar to yours matters. Ask for:

1. Specific case studies with measurable outcomes (task completion rate, latency, cost per request).

2. References you can call about integration, uptime, and support.

3. A portfolio that shows work on the same data types and regulatory environment you operate in (healthcare, finance, etc.).

Evaluation frameworks (beyond unit tests)

Good partners use rigorous evaluation beyond simple accuracy metrics. Look for teams that apply specialized evals measuring:

• End-state outcomes and task completion,

• Interaction quality (human-in-the-loop tests),

• Safety tests (hallucination rates, policy violations),

• Continuous monitoring metrics after deployment.

Explore OpenAI’s Evals repository as an example of tooling designed for structured model evaluation; ask if the partner builds similar evaluative suites for your specific tasks.

Secure, staged development with sandboxes

Choose partners who use layered sandboxes and stage-gates: ideation → development sandbox → integration sandbox → pre-production → production. Staging lets you test data flows, prompts, tool access, and safety controls without risking production data or users.

Data governance, privacy, and compliance

Make sure the partner can:

• Enforce least-privilege access and encryption in transit and at rest,

• Implement data retention, deletion, and audit trails,

• Map where data flows (so you can assess regulatory exposure),

• Support contractual and technical controls required by your regulators.

Ask for documented controls and third-party audit reports (SOC 2, ISO 27001) if they handle sensitive data.

Post-deployment support, monitoring, and iteration

Agents degrade if data distributions shift or new failure modes appear. Vet the partner for:

• Proactive monitoring (latency, error rates, drift metrics),

• Incident response and rollback procedures,

• Ongoing fine-tuning and prompt/chain-of-tooling updates,

• Training and knowledge transfer so your team can take ownership.

Performance and observability should be part of the SLA.

Pricing and engagement models: hybrid, in-house, or outsourced

There’s no one-size-fits-all. Common models:

• Proof-of-concept with a boutique partner (fast validation, lower cost),

• Hybrid (vendor helps build and you transition to in-house ops),

• Managed service (vendor operates the agent in production).

Many vendors and consultants report pilot timelines measured in weeks to a few months, with enterprise production projects taking longer. Expect pilot timelines around ~2 months for an MVP, and budgets for small-to-medium agent projects frequently fall between roughly $30 K and $200 K depending on scope, integrations, and compliance needs — confirm estimates with detailed scoping.

For strategic scaling and timelines, McKinsey outlines organizational steps and expected durations.

Ethical development and responsible AI practices

Ask the partner how they address bias, transparency, and user consent:

• Do they run bias audits and impact assessments?

• What guardrails and human-in-the-loop controls do they implement?

• How do they handle explainability and user notifications?

Look for documented processes and artifacts (model cards, data sheets) that show thought given to these issues.

Practical checklist to vet a partner (use during calls)

• Can you show a case study that matches our domain? (ask for metrics)

• Do you build models in-house or fine-tune third-party models? Which licenses apply?

• How do you pipeline and secure our data? Which audit reports do you have?

• What evals and safety tests will you run, and can we see sample reports?

• What sandboxes and stage-gates do you use for dev → prod?

• What’s included in post-launch support and monitoring? SLA details?

• What are the estimated timelines and cost ranges for an MVP and production?

• Who owns IP and model artifacts after delivery?

Quick scoring rubric you can use right away

1. Alignment to goals (0–5)

2. Data engineering capability (0–5)

3. Licensing transparency (0–5)

4. Evals & safety testing (0–5)

5. Deployment & ops readiness (0–5)

6. Cost clarity and engagement fit (0–5)

A partner scoring ≥20/30 is worth deeper conversations and a reference check.

Your next move (a practical closure)

Pick one internal owner, document the agent’s top 3 success metrics, and run short discovery calls with 3 partners using the checklist above. Ask each to present a one-page plan that covers model sourcing, data architecture, testing/sandbox approach, costs, and an 8-week pilot plan. That one-page plan will quickly reveal who understands the real work behind AI agents and who is selling buzzwords.