Engaging external partners to build AI agents is increasingly common. The technology is specialized enough that many organizations lack internal expertise, yet the business value is significant enough to pursue.

This guide covers how to work effectively with team400.ai and similar firms to maximize project success.

Before Engagement: Preparation

Work before engaging a builder improves outcomes:

Define the problem clearly: What business problem are you solving? What processes will the agent handle? What does success look like?

Document current state: How does the process work today? What are volumes, error rates, costs? This baseline enables meaningful evaluation.

Identify stakeholders: Who’s affected by the agent? Who needs to be involved in design and approval? Whose buy-in is essential?

Assess data availability: What data will the agent need? Is it accessible? What’s the quality? Data problems are the most common project blockers.

Set realistic expectations: Agent projects take time. Prepare stakeholders for an iterative process, not a one-time delivery.

Selection: Choosing a Partner

Evaluating potential agent builders:

Technical competence: Do they understand current AI capabilities and limitations? Ask specific technical questions about approaches, architectures, and tradeoffs.

Relevant experience: Have they built similar agents? References and case studies matter more than credentials.

Process clarity: Do they have a defined methodology? Can they explain their approach clearly?

Communication style: How do they communicate? Do you understand their explanations? Good partnership requires good communication.

Cultural fit: Will they work well with your team? Technical excellence with poor collaboration isn’t effective.

Business model: How do they price work? What’s included and excluded? Are incentives aligned with your success?

During Engagement: Collaboration

Effective collaboration during the project:

Stay involved: Agent projects need business input throughout. Don’t delegate completely and expect good results.

Provide access: Agents need data, systems, and subject matter experts. Delays in access delay the project.

Give feedback promptly: When you see outputs, respond quickly. Slow feedback extends timelines.

Share context: Information that seems obvious to you may not be to the builder. Over-communicate about business context.

Manage stakeholders: Keep affected people informed and engaged. The builder can’t do this alone.

Address blockers: When the builder identifies problems, work to resolve them. Don’t let issues fester.

Discovery Phase: Setting Foundations

The discovery phase defines project direction:

Process deep-dives: Detailed understanding of current workflows, variations, and edge cases.

Data assessment: Evaluating data quality, accessibility, and gaps.

Technical evaluation: Understanding existing systems and integration requirements.

Scope definition: Clear boundaries on what the agent will and won’t do.

Success criteria: Measurable outcomes that define project success.

Risk identification: What could go wrong? How will risks be managed?

Rushing discovery to start building faster usually backfires. Invest time here.

Development Phase: Building the Agent

During development:

Expect iteration: First versions won’t be perfect. Plan for refinement cycles.

Participate in testing: Your team should test agents, not just the builder. Different perspectives catch different issues.

Maintain communication: Regular check-ins keep everyone aligned. Issues caught early are easier to address.

Watch for scope creep: New requirements naturally emerge. Evaluate whether they’re essential or can wait for later phases.

Track progress honestly: If the project is struggling, acknowledge it early. Denial makes problems worse.

Integration: Connecting Systems

Integration typically consumes significant effort:

System access: Providing the builder with access to integrate with your systems.

API coordination: Working with internal teams to expose necessary APIs or data feeds.

Security review: Ensuring integrations meet security requirements.

Testing environments: Providing environments that adequately simulate production.

Data validation: Confirming data flows are accurate and complete.

Anticipate integration being harder and slower than expected. Plan for it.

Deployment: Going Live

Moving from development to production:

Staged rollout: Start with limited scope. Expand based on performance.

Monitoring setup: Ensure visibility into agent operations before going live.

Escalation paths: Define how issues get reported and addressed.

User communication: Prepare users for the new agent. Set appropriate expectations.

Parallel operation: Consider running agents alongside existing processes initially.

Post-Deployment: Sustaining Value

After launch:

Monitor outcomes: Track whether the agent delivers expected business value.

Collect feedback: Systematic capture of user and stakeholder feedback.

Address issues promptly: Problems in production should be fixed quickly.

Plan improvements: Agents improve over time. Budget for ongoing development.

Knowledge transfer: Ensure your team can maintain and extend the agent.

Common Pitfalls

Avoid these mistakes:

Underestimating complexity: Agent projects are harder than they look. Plan for challenges.

Ignoring change management: Technology works but people don’t adopt it. Include adoption planning.

Over-specifying upfront: Detailed specifications often prove wrong. Stay flexible.

Under-specifying upfront: Complete ambiguity also fails. Find the right level of definition.

Treating as IT project: Agent projects need business involvement throughout, not just at the beginning and end.

What to Expect from Good Partners

Quality agent builders:

• Ask hard questions and push back when scope is unclear
• Communicate proactively about progress and problems
• Deliver working software frequently, not just at project end
• Document their work for knowledge transfer
• Stay engaged through adoption, not just delivery

My Perspective

Working with AI agent builders is a partnership, not a purchase. Your organization must invest time, attention, and resources throughout the engagement.

The best outcomes come from organizations that treat agent development as a joint venture—staying engaged, providing resources, and taking responsibility for adoption alongside the builder’s responsibility for technology.

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A practical guide to working effectively with AI agent builders.