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ai-agents

21 articles

Building agents that maintain context, coordinate handoffs, and remember users across sessions. Multi-agent systems fail at personalization because they share tools, not understanding.

Essential reading
The Warm Handoff Problem in AI
Opinion ai-agents context

The Warm Handoff Problem in AI

When an AI agent hands off to another agent, or to a human, context dies. The warm handoff problem is the biggest unsolved UX issue in multi-agent systems, and self-models are the fix.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 8 min read
Identity Drift in Long-Running AI Agents
Deep Dive ai-agents self-models

Identity Drift in Long-Running AI Agents

Long-running AI agents gradually lose coherent user understanding through identity drift. Self-models anchor agent behavior to structured, evolving user identity.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 14 min read

All articles
AI Agent Evaluation: Building a Testing Framework That Works
Deep Dive evaluation ai-agents

AI Agent Evaluation: Building a Testing Framework That Works

A complete framework for evaluating AI agents in production: failure taxonomies, grading rubrics, automated eval pipelines, and alignment scoring.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 20 min read
The Agent Memory Problem Nobody Is Solving
Opinion ai-agents memory

The Agent Memory Problem Nobody Is Solving

Agent memory architectures require persistent self-models that maintain belief states across sessions and agents, not just chat logs or vector storage. Enterprise teams building multi-agent systems face alignment drift and coordination taxes when memory lacks shared contextual understanding.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 8 min read
Multi-Agent Architectures: Orchestration Patterns That Work in Production
Deep Dive ai-agents architecture

Multi-Agent Architectures: Orchestration Patterns That Work in Production

Supervisor, router, chain, and consensus patterns for multi-agent systems. Failure modes, recovery strategies, and production code.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 14 min read
Deterministic vs. Agentic AI Pipelines: When to Use Each
Deep Dive architecture ai-agents

Deterministic vs. Agentic AI Pipelines: When to Use Each

When deterministic pipelines beat agentic ones and vice versa. Decision framework, hybrid patterns, and code examples for production systems.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 12 min read
The Alignment Check Before Every PR
Opinion developer-tools alignment

The Alignment Check Before Every PR

What if every pull request included an automated alignment check, verifying not just that the code works, but that it moves the product closer to what users actually need?
Robert Ta's Self-Model
Robert Ta's Self-Model
· 10 min read
Agent Context Decay: Why Your AI Gets Worse Over Time
Opinion ai-agents context-management

Agent Context Decay: Why Your AI Gets Worse Over Time

AI agents degrade in production not from model decay but context management failure. Self-models solve context decay with structured user understanding.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 11 min read
AI Agent Testing: Failure Taxonomies That Actually Work
Deep Dive ai-agents evaluation

AI Agent Testing: Failure Taxonomies That Actually Work

Six failure categories for AI agent evaluation with grading rubrics and automated eval patterns. Practical testing for production agents.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 13 min read
AI-Generated Code Needs AI-Generated Reviews
Opinion ai-agents developer-tools

AI-Generated Code Needs AI-Generated Reviews

Code review is the last human bottleneck in AI-accelerated development. The answer is not removing humans from reviews, it is giving reviewers an AI that understands the codebase the way they do.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 11 min read
Belief State vs Session State: What Your Agent Should Track
Opinion ai-agents self-models

Belief State vs Session State: What Your Agent Should Track

Session state resets. Belief state compounds. Agents that track what users know, want, and prefer grow more effective over time.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 10 min read
Context Windows Are Not Memory
Opinion ai-agents memory

Context Windows Are Not Memory

Your AI agent has a 128K context window. You think it remembers. It does not. Context windows are temporary buffers, not persistent understanding. Here is why the distinction matters and what to do about it.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 10 min read
The Hidden Cost of AI Hallucinations in Enterprise Workflows
Deep Dive enterprise-ai evaluation

The Hidden Cost of AI Hallucinations in Enterprise Workflows

AI hallucinations in enterprise systems are not just embarrassing — they are expensive. Here is how to quantify and reduce the damage.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 9 min read
Multi-Agent Memory Benchmarks
Research ai-agents evaluation

Multi-Agent Memory Benchmarks

Multi-agent systems are everywhere. Memory in multi-agent systems is nowhere. We benchmarked how well leading agent frameworks retain user context across agent handoffs - and the results are concerning.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 13 min read
5 Production AI Failure Patterns Enterprise Teams Miss
Guide ai-products strategy

5 Production AI Failure Patterns Enterprise Teams Miss

Your AI chatbot is live but underperforming. Here are 5 warning signs it needs expert intervention — and what to do about each one.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 8 min read
The Epistemic Intelligence Framework: How AI Agents Should Model What They Know and Do Not Know
Opinion epistemic-intelligence ai-agents

The Epistemic Intelligence Framework: How AI Agents Should Model What They Know and Do Not Know

Most AI agents act as if they know everything or nothing. Epistemic intelligence is the ability to model uncertainty, track belief states, and calibrate confidence, the missing layer in agent architecture.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 11 min read
Agent Evaluation at Enterprise Scale: Beyond Vibes-Based QA
Research evaluation ai-agents

Agent Evaluation at Enterprise Scale: Beyond Vibes-Based QA

Most AI agent evaluation is vibes-based, someone checks a few outputs and says 'looks good.' At enterprise scale, you need structured evaluation that measures alignment, not just accuracy.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 10 min read
Scaling Agents from 1 to 50 Without Losing Context
Deep Dive ai-agents architecture

Scaling Agents from 1 to 50 Without Losing Context

Multi-agent systems lose user context at handoff boundaries. Self-models provide a shared understanding layer that scales agent architectures without context loss.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 13 min read
Agent Memory: Why Chat Logs Are Not Enough
Deep Dive ai-agents memory

Agent Memory: Why Chat Logs Are Not Enough

Chat logs give AI agents history but not understanding. Agent memory needs structured self-models that capture beliefs, goals, and context evolution.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 15 min read
The User Model Gap in Agentic Workflows
Opinion ai-agents self-models

The User Model Gap in Agentic Workflows

Agentic AI workflows model tasks, tools, and plans, but not the user. Self-models give agents a persistent user layer that survives across sessions and handoffs.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 11 min read
Why Every AI Agent Needs a User Model
Opinion ai-agents self-models

Why Every AI Agent Needs a User Model

AI agents are getting tools, memory, and planning. But they are missing the one thing that makes their actions useful: understanding who they are acting for. Every agent needs a user model.
Robert Ta's Self-Model
Robert Ta's Self-Model
· 12 min read

Related topics
memory (6) architecture (76) self-models (114)

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