Why We Built AI Agents—and Then Pulled the Plug
Key takeaways
- Autonomous AI agents currently suffer from hallucinations, brittle tool‑calling, and security challenges.
- The cost of maintaining unreliable agents can outweigh their productivity benefits.
- Human‑in‑the‑loop designs provide higher trust and better compliance than fully autonomous agents.
- Observability, auditability, and cross‑team alignment must be built into the system from day one.
- Start with narrowly scoped use‑cases and iterate toward broader automation.
When we first set out to create AI agents at Runnit, the vision was simple: give businesses a self‑service assistant that could understand intent, navigate complex workflows, and execute tasks without human oversight. The hype around large language models (LLMs) like GPT‑4 and the rise of “agentic AI” made it feel inevitable that a few months of development would yield a product that could replace countless manual processes.
Our roadmap was ambitious:
1. Natural‑language orchestration – users type a request, the agent decomposes it into subtasks. 2. Tool integration – the agent calls APIs, updates spreadsheets, and triggers webhooks. 3. Continuous learning – feedback loops refine the agent’s decision‑making over time.
We assembled a cross‑functional team of engineers, product designers, and data scientists. The early prototypes were thrilling. A single prompt could generate a weekly report, schedule meetings, and even draft a follow‑up email. It felt like the future we’d been waiting for.
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The Cracks Appear
1. **Context‑drift and hallucinations**
LLMs are spectacular at generating fluent text, but they lack a reliable grounding in reality. Our agents would sometimes hallucinate data—creating fictitious invoice numbers or misreading a client’s name. Even with guardrails like schema validation, the problem persisted because the model’s internal reasoning was opaque.
2. **Tool‑calling brittleness**
Integrating with external services required precise JSON payloads. A single missing field caused the entire chain to fail, and the agent had no graceful fallback. We tried to mitigate this with retry logic, but the root cause was the model’s inability to verify its own output before execution.
3. **Security and compliance concerns**
Autonomous agents that can read, write, and delete data raise red‑flag questions for GDPR, HIPAA, and corporate governance. Our legal team demanded exhaustive audit trails and role‑based access controls—features that dramatically increased engineering overhead.
4. **User trust and expectations**
Early adopters loved the novelty, but as the agents stumbled, confidence eroded. A single erroneous transaction could cost a client hours of manual correction, undermining the very efficiency the agents promised.
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The Decision to Delete
By month six, the cost‑benefit analysis was stark:
- Engineering effort: ~1,200 person‑hours per quarter for maintenance and bug‑fixes. - Support load: 30% increase in tickets related to agent failures. - Opportunity cost: Delaying other high‑impact features like real‑time analytics.
We held a series of post‑mortems with stakeholders from product, engineering, sales, and compliance. The consensus was that the agents, in their current form, were a technical liability rather than a strategic asset.
So we made the hard call: decommission the agents, archive the code, and refocus on building human‑in‑the‑loop workflows that leverage LLMs for suggestion rather than autonomous execution.
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What We Learned
• **Agentic AI is still nascent** The technology is powerful, but reliability at scale remains an open problem. Until we have robust mechanisms for verification, grounding, and error recovery, fully autonomous agents are risky for production environments.
• **Design for observability from day one** Every decision the agent makes must be logged, versioned, and auditable. Retrofitting observability after the fact is a nightmare.
• **Human‑centered augmentation beats full automation** Providing *suggestions* that a user can approve yields higher trust and lower error rates. This hybrid model aligns better with compliance requirements.
• **Cross‑team alignment is critical** Legal, security, and ops need to be involved early. Their constraints shape the feasibility of any autonomous system.
• **Iterate on narrow use‑cases first** Instead of a “do‑everything” agent, start with a single, well‑defined task (e.g., invoice reconciliation) and perfect it before expanding.
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Looking Forward
We haven’t abandoned AI; we’ve simply re‑calibrated our approach. Our roadmap now includes:
- Prompt‑engineering frameworks that let users craft safe, reproducible instructions. - Tool‑calling sandboxes that simulate API interactions before committing changes. - Feedback loops where the model proposes an action, the user confirms, and the system learns from the approval.
The experience taught us that building AI agents is less about writing code and more about architecting trust. When the stakes are high—financial data, patient records, or critical business workflows—trust cannot be an afterthought.
If you’re considering a similar venture, start small, embed rigorous validation, and be prepared to pivot quickly. The future of AI‑driven automation is bright, but it will be built on responsibility, transparency, and human oversight.
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If you’d like to discuss how Runnit can help you integrate LLM‑powered assistance safely into your workflow, feel free to reach out.
Sources: https://runnit.io/articles/we-spent-months-building-ai-agents-into-runnit-then-we-deleted-them