We Built AI Agents. They Failed in the Real World

Agents reason from data. When the data is inconsistent, incomplete, or siloed across systems that use different identifiers for the same entity, the agent's outputs are wrong in proportion to the data's defects. Deloitte's 2025 survey found that nearly half of organisations cited data searchability and reusability as primary challenges to their AI automation strategy. These are organisations that had already committed AI budgets and were discovering that their data was not positioned to be consumed by agents that need business context to make decisions.The specific data quality issues that most commonly destroy agent accuracy in production: inconsistent entity naming across systems (the same SKU named differently in the WMS and the OMS a join that fails every time), stale reference data (commission rate tables that have not been updated after the latest marketplace rate change), and missing transactional linkages (return records in one system with no corresponding record in the connected system). Each issue is individually small. Collectively, they produce an agent whose outputs the team cannot trust and an agent whose outputs cannot be trusted will not be used.

Most enterprise AI pilots are designed to succeed. The problem is scoped tightly. The data is cleaned before the pilot starts. A motivated champion manages the implementation. The metrics are chosen to show the technology at its best. The pilot succeeds. Then the team attempts to hand off to the production environment the messy version of the system the pilot was isolated from and the handoff fails. The production team inherits a system optimised for clean conditions, with no documentation of the workarounds that made it function, no ownership model for ongoing maintenance, and no budget for the data quality remediation the pilot team handled informally.MIT's research found that most AI projects stall in the proof-of-concept stage with no clear owner, no economic model for scaling, and unresolved data problems. The organisations that successfully cross the pilot-to-production gap assign a named owner for production outcomes before the pilot begins, document every workaround the pilot team applied, and treat production system integration not demo system integration as a required deliverable before the pilot is declared successful.

Agentic systems that act autonomously require governance frameworks that most organisations have not built before deploying them. When an agent makes a wrong decision at scale generating incorrect purchase orders, submitting erroneous dispute filings, executing campaign adjustments based on corrupted data the absence of audit trails, approval gates, and rollback capabilities means the damage cannot be contained, the root cause cannot be traced, and the trust destruction is immediate and severe.The organisations that deploy agents without governance frameworks consistently report the same sequence: a single high-visibility error within the first month of autonomous operation, a rollback to human-supervised mode, and a six-to-twelve-month pause while governance is built retrospectively. Building governance before deployment is not bureaucracy. It is the difference between an agent error that is corrected in four minutes and an agent error that poisons the organisation's willingness to deploy AI autonomously for a year.

Organisations that anchor their AI strategy to a specific vendor's product roadmap inherit every pivot that vendor makes. Salesforce cut 4,000 customer support roles citing Agentforce's capability then cut members of the Agentforce team itself three months later as the product direction shifted. The organisations generating the strongest AI ROI build their strategy around specific business outcomes they own and can measure, using platforms as infrastructure rather than as the strategy itself. When the vendor's product evolves and it always does a business-outcome-anchored strategy survives. A vendor-roadmap-anchored strategy does not.

• Define the specific business problem before selecting the vendor not 'we need AI agents' but 'we lose an estimated ₹45 lakhs per quarter in unreconciled settlement discrepancies and we need a system that catches them automatically'
• Conduct a data quality assessment for the first agent's data sources before deployment and resolve the specific issues that will cause incorrect outputs not a full enterprise data programme, targeted remediation of the fields the agent will actually touch
• Assign a named owner for production outcomes whose performance evaluation includes whether the deployment delivers its stated objective
• Build audit trails, approval thresholds, and rollback capabilities as deployment prerequisites governance retrofitted onto a running production system costs three to five times more than governance built before deployment
• Test against actual production data, not demo data, before the pilot is declared successful and used as justification for expanding the deployment