Why Most AI Agents Fail in Production & How We Built One That Doesn’t

Most AI agent projects fail because they chase demos—not durability. We’ve been there: a flashy GPT prototype that impresses in a meeting but falls apart when exposed to real users, data, and operations.

At Tekunda, we wanted more than a gimmick—we wanted a production-grade AI agent that could automate outbound prospecting at scale, integrate with CRMs, reason over context, and actually drive revenue. That’s why we built an AI-powered outbound engine, fully automated across LinkedIn and Salesforce and enriched with real-time intelligence using Langflow, Python, and our internal MCP servers.

This is how we did it, and how you can too.

Step 1: Automate the Front Door—LinkedIn Outreach with Precision.

It all starts with a lead list from LinkedIn—whether it’s a search result or a Sales Navigator saved list. We pass this directly into Linked Helper, which automates:

• Connection invites (within LinkedIn’s 200/week/user limit)
• Thank-you follow-ups
• “Follow company page” nudges
• CRM sync (in our case, Salesforce)

Once a new connection is made and synced into the CRM, our AI agent kicks in.

Step 2: Use Langflow and Custom Python for Orchestration.

After a contact lands in Salesforce, our AI agent uses past lead data—conversion history, success criteria, and segment traits—as RAG context to reason over new leads.

We orchestrate all logic using Langflow, backed by custom Python flows and MCP servers that manage API calls, enrichments, and prompt execution across tools.

Step 3: Real-Time Enrichment + Ranking

Once leads are synced:

• The agent enriches each lead with verified work emails using tools like Findymail.
• It pulls signal data like job changes, funding, or tech stack from public sources using OpenAI Search API or Perplexity.
• Then, it scores each lead out of 100 based on similarity to past wins, giving a detailed explanation for each score.

No human tagging, no spreadsheets—just fully automated qualification.

Our RAG pipeline uses custom embedding models + Langflow chains, with chunking and retrieval strategies tuned for lead qualification tasks. We tested both vector stores and PostgreSQL-based retrieval and selected based on performance and cost.

Step 4: Crafting and Sending Personalised Outreach

For every lead scoring above 50, the agent:

• Generates a hyper-personalised cold email, drawing on company info, role signals, and recent activity.
• Includes a tailored CTA to schedule a call, aligned with what’s worked in previous wins.
• Pushes email data back into Salesforce for tracking and next steps.

The system adapts weekly, learning from which emails convert and which don’t—thanks to a continuously closed feedback loop.

This layer is powered by a Langflow-driven agent with modular prompt templates, context-conditioned using historical success patterns. The entire flow is stateless and handled via orchestrated micro-flows, which allows us to reuse and scale across campaigns.

Step 5: Scale Intelligently

Yes, LinkedIn throttles invites to ~200 per week per user. But that’s just the top of the funnel.

MCP allows us to dynamically queue enrichment and email generation tasks, ensuring no bottlenecks. We use custom logging and monitoring tools, plus Langfuse, to debug and optimize agent decisions continuously.

Lead enrichment, scoring, and outbound email generation are uncapped—scaling as fast as your team can grow. Need more reach? Just add LinkedIn seats or upgrade to a higher Tekunda AI tier.

Continuous Monitoring and Improvement

We follow best practices for observability—with logs, structured error handling, prompt diffing, and outcome analysis embedded into the core agent flow. This allows us to ship updates fast and confidently.

• We monitor every agent output using Langfuse and internal dashboards.
• User feedback and conversion signals are captured and looped back into the ranking + prompt layers.
• We iterate weekly—refining prompt logic, retraining embeddings, and tuning scoring heuristics.

The Bottom Line

Most AI agents stay stuck in prototype purgatory because they’re built for show, not scale. At Tekunda, we built one that thinks, adapts, and converts—by combining

• ✅ Real orchestration using Langflow and Python
• ✅ Full integration with CRM+MCP servers for enrichment.
• ✅ Feedback loops and lead scoring that learn over time
• ✅ No manual handoff—from invite to booked call

The end result: a self-operating outbound machine that keeps your pipeline full while your team focuses on closing.

Want this for your business?

Ask us about Tekunda’s outbound AI agents and see how we can automate your lead generation, qualification, and outreach—end to end, tailored to your ICP and growth goals.