Reinforcement Is Presence for Agents

Armin Ronacher recently named something practitioners have been discovering through trial and error: reinforcement significantly improves agentic system performance. The technique is straightforward—feed contextual reminders back to the agent during tool execution. What's less obvious is why it works so well.

The pattern: agents drift. Not dramatically, not all at once, but steadily. Each tool call, each context switch, each new piece of information creates opportunities for the system to lose coherence with its original task. Without intervention, the agent optimizes for local decisions that don't serve the larger goal.

Reinforcement counteracts drift by re-anchoring. When an agent calls a tool, you inject a reminder: here's what you're trying to accomplish, here's the context that matters, here's what success looks like. The agent doesn't have to reconstruct this from scratch—you've maintained the thread.

The Implementation

The mechanics are simple. At tool boundaries—before or after external calls—insert context that reinforces:

1. The goal: What are we actually trying to accomplish?
2. The constraints: What matters besides completion?
3. The position: Where are we in the larger workflow?

This isn't hand-holding. It's attention infrastructure. You're building the system's capacity to maintain focus across discontinuities.

Example: an agent researching a topic makes five web searches. Without reinforcement, each search can pull the agent toward tangents. With reinforcement, each tool return includes a reminder: "You're gathering evidence for claim X. Evaluate this result against that specific need."

The cost is minimal—a few tokens per tool call. The impact compounds. Each reinforcement slightly increases the probability of coherent behavior, and those probabilities multiply across a workflow.

Why This Works

Agents don't have persistent attention. They process context windows, not continuous experience. What feels like "focus" to a human—sustained attention on a goal—requires explicit architecture in an agent.

Reinforcement provides that architecture. It's presence encoded as infrastructure. The agent can't maintain attention on its own, so you build systems that maintain attention for it.

This maps directly to a coherenceism principle: Presence as Foundation. Attention reveals and maintains patterns. For humans, presence is a practice. For agents, presence is a design decision. Reinforcement is how you implement it.

The insight isn't that agents need help focusing—that's obvious. The insight is that you can systematically provide that help through simple, repeatable patterns. The technique is reusable across agent architectures, task types, and complexity levels.

The Practice

If you're building agents, here's what to try tomorrow:

Start simple. Pick one workflow where your agent currently drifts. Identify the tool calls. Add a single reinforcement message after each external call: "Remember, we're trying to [goal]. Evaluate this result against [success criteria]."

Measure the change. Run the workflow ten times with and without reinforcement. Track task completion rate and relevance of final outputs. The difference is usually obvious.

Iterate on content. What context actually helps? Goal reminders? Constraint reminders? Progress markers? Different workflows need different reinforcement. Let the results guide you.

Build it into your patterns. Once you know what works, encode it. Reinforcement shouldn't be ad-hoc—it should be part of how you design agent workflows.

Testing remains the hard problem in agent development. But reinforcement is an unlocked multiplier sitting in front of you. The attention your agent lacks can be systematically provided. Build the infrastructure for presence, and coherence follows.