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Building AI Agents: Why reliable communications data matters
• 6 min read
AI agents are now running inside real products. Teams benchmark models, tune prompts, and stitch together orchestration layers, but the most common failure point appears much earlier in the stack: the data. Systems break when the information underneath them is inconsistent, incomplete, or unsafe to act on.
No model performs well when its inputs cannot be trusted.
Communications data makes this problem especially challenging. Every major email, calendar, and meeting provider exposes different schemas, undocumented fields, inconsistent threading logic, and different limits for pagination, history, and metadata retention. Recurring meetings are handled differently across providers. Identity behaves differently across platforms. Transcription systems return different structures, levels of fidelity, and speaker-attribution formats.
When AI agents rely on this data, small inconsistencies become major reasoning failures. This is where agent systems fail in production. Not with dramatic outages, but through gradual drift as edge cases multiply and workflows degrade over time.
Most APIs were designed for humans and request–response interactions. AI agents do not behave like humans. They run continuously, retry aggressively, follow branching logic, and take autonomous actions. Those behaviors expose assumptions that most integrations depend on.
Authentication flows that work in development break under load. Providers throttle traffic unpredictably. Calendar updates fire conflicting events. Email systems silently strip or alter metadata. Message IDs behave differently across providers. Some calendars allow overrides; others reject them. And upstream schema changes roll out without notice.
Security and privacy multiply these challenges. Agentic systems introduce access patterns that were never anticipated by legacy APIs: high-frequency reads, autonomous writes, broad visibility across inboxes or calendars, and automated processing of sensitive content. Without strict permissions, predictable scopes, and auditability, an agent can overreach or leak data simply by doing what it was designed to do.
None of this is abnormal. It is the natural result of infrastructure built for people now being used by machines that act at far greater scale and speed.
Because communications systems encode commitments, intent, and relationships, failures in this layer are not cosmetic; they change system behavior.
The 2025 Postman State of the API Report reflects what engineering teams are already experiencing. AI adoption is accelerating faster than any platform shift in recent memory, yet API readiness for autonomous systems remains low. Governance, unsafe retries, provider drift, and lack of machine-readable contracts are now top concerns. Early standards like Model Context Protocol (MCP) are emerging, but widespread adoption is still early.
The conclusion is clear: AI is moving faster than the infrastructure beneath it. This is not a tooling issue; it is an architectural one.
Most conversations about AI focus on models or orchestration. Very few address the foundation: the data entering those systems.
For AI agents to operate safely and reliably, communications data must be normalized across providers, fail predictably, enforce least-privilege access, carry consistent metadata, and generate audit-ready events. None of this comes from model selection or prompt tuning. It comes from infrastructure built to absorb upstream volatility.
Nylas has spent more than a decade solving these problems in production across email, calendar, and meeting platforms: normalizing schemas, reconciling identities, absorbing rate limits, mapping undocumented provider behavior, retrying safely, delivering consistent events despite inconsistent upstream systems. These are not features — they are the baseline required for agentic systems to function.
Failures rarely surface as clear errors. They show up as subtle inconsistencies that compound:
These are not model problems.
They are ingestion, trust, and execution problems.
Agents do not fail at reasoning. They fail because the data they rely on is unreliable.
Nylas is not an AI layer. It’s the communications infrastructure that AI depends on.
We provide a stable, normalized layer across email, calendar, and meeting data, plus OAuth handling, consistent schemas, reliable event delivery, and protection from undocumented provider differences. This is the layer autonomous systems rely on when they observe and act.
Security and compliance are also core to this foundation. Nylas enforces least-privilege OAuth scopes across providers, applies consistent permission controls, generates audit-ready events, and isolates data access so agents cannot overreach or leak sensitive information. Because these controls are built into the infrastructure rather than added later, teams can unlock agentic automation without introducing new governance or privacy risk.
Nylas also meets the security standards enterprises expect. We maintain SOC 2 Type II, ISO 27001, HIPAA-eligible services, and a full suite of compliance controls that support regulated workloads. These certifications, combined with our dedicated security program and continuous monitoring, allow teams to deploy agentic systems without introducing new risk. You can review our full security posture at https://stg-5ji7vw.elementor.cloud/security/.
On top of that foundation, teams build the workflows that drive real value:
The goal is not to create “smarter models.”
The goal is to create systems that behave correctly.
AI is moving from summarizing workflows to running them.
Systems send emails, schedule meetings, update records, route tasks, and coordinate people. At this point, communications stops being an integration challenge and becomes infrastructure.
Every AI product that touches inboxes, calendars, or scheduling eventually hits the same wall:
you cannot reason without structure, you cannot operate safely without controls, and you cannot scale without normalization.
If your agent interacts with email, calendars, or meeting data, the hardest part is not the model — it is the data.
If you want agents that operate reliably in production, start with a communications infrastructure built for machine-scale behavior, not human workflows.
If your workflows begin with email, Nylas Inbound provides a programmable signal layer that turns raw messages into normalized, structured events your systems can act on: https://inbound.nylas.com/
Explore the Nylas APIs: https://developer.nylas.com/
Get started: https://dashboard-v3.nylas.com/register
This post is part of our “Building AI Agents” series.
If you haven’t yet, read the other entries:
In our CTO’s post, we argued that AI systems fail when the data they depend…
In our CTO’s post, we argued that trustworthy communications data is the foundation for AI…
TL;DR Everyone’s doubling down it. SaaS users want to see more meeting recording capabilities in…
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